AI Jobs in Abu Dhabi

Lead Machine Learning Engineer | AI Lab | Abu DhabiWe’re hiring a Lead Machine Learning Engineer to join a high-growth AI Lab in Abu Dhabi, working on the design, development, and deployment of advanced machine learning solutions at scale.This is a hands-on technical leadership role for an experienced ML Engineer who can take models from research and experimentation through to production, while helping shape engineering standards and mentor other members of the team.Key ResponsibilitiesLead the design, development, and deployment of production-grade machine learning systemsBuild and optimise ML models across a range of AI use casesOwn the full ML lifecycle, from experimentation and model development through to deployment and monitoringDesign scalable ML pipelines and infrastructure for training and inferenceWork closely with Data Scientists, ML Engineers, Software Engineers and Product teamsDrive best practices across MLOps, model performance, testing and production reliabilityProvide technical leadership and mentorship to engineers within the teamEvaluate new models, frameworks and technologies and introduce them where appropriateRequirements8+ years of experience across Machine Learning, Software Engineering or related technical rolesStrong hands-on experience building and deploying machine learning models into productionAdvanced Python development skillsStrong understanding of machine learning algorithms, deep learning and modern AI architecturesExperience with frameworks such as PyTorch, TensorFlow or similarExperience building scalable ML pipelines and working with large datasetsStrong understanding of MLOps, model deployment, monitoring and CI/CDExperience with cloud platforms such as AWS, Azure or GCPStrong software engineering fundamentals and experience designing production-grade systemsPrevious experience leading technical projects and mentoring engineersBachelor’s or Master’s degree in Computer Science, Machine Learning, AI or a related field

En Decide4AI buscamos un/a Senior Data Scientist con disponibilidad para trabajar presencialmente en Abu Dhabi hasta, al menos, principios de 2027.Formarás parte de un programa estratégico de Transformación Digital e Inteligencia Artificial para una de las compañías energéticas más importantes del mundo, trabajando junto a equipos internacionales en iniciativas con impacto global y visión a largo plazo.Buscamos una persona práctica, versátil y orientada a la ejecución, capaz de participar en todas las fases de una solución de Data Science.🚀 ¿Qué harás?👉 Trabajar directamente con stakeholders del cliente para entender retos de negocio y traducirlos en soluciones analíticas.👉 Diseñar y desarrollar soluciones de Data Science end-to-end, desde la exploración de datos hasta la puesta en producción.👉 Construir modelos de Machine Learning, pipelines de datos y soluciones analíticas escalables.👉 Participar en proyectos de Data Engineering, Machine Learning, Optimización e IA Generativa.👉 Colaborar con equipos multidisciplinares de Data Science, Ingeniería y Negocio.👉 Comunicar resultados e impacto a perfiles técnicos y ejecutivos (C-level).🤝 ¿Cómo trabajarás?Te integrarás en un equipo internacional de aproximadamente 15 profesionales trabajando desde las oficinas del cliente en Abu Dhabi.El trabajo es principalmente presencial, colaborando estrechamente con cliente y equipos técnicos en un entorno altamente dinámico, multicultural y orientado a resultados.Trabajarás sobre tecnologías y proyectos relacionados con Data Science, Data Engineering, Machine Learning, Optimización e Inteligencia Artificial, utilizando principalmente Python, tecnologías cloud y arquitecturas modernas de datos.🎯 ¿Qué buscamos?✅ Nivel de inglés equivalente a C2 (imprescindible).✅ Disponibilidad para trabajar presencialmente en Abu Dhabi.✅ Experiencia sólida desarrollando soluciones de Data Science en entornos reales de negocio.✅ Experiencia trabajando directamente con clientes o stakeholders.✅ Excelente capacidad de comunicación y resolución de problemas.✅ Dominio de Python, Machine Learning y procesamiento de datos.✅ Experiencia con plataformas cloud (Azure, AWS o GCP).✅ Capacidad para combinar análisis, desarrollo, modelado y puesta en producción de soluciones.✅ Curiosidad, iniciativa, capacidad de adaptación, capacidad de aprendizaje rápido y orientación a resultados.✅ Comodidad trabajando en entornos ambiguos, dinámicos y altamente colaborativos.🌍 ¿Qué ofrecemos?✨ Participar en uno de los programas de Transformación Digital e IA más ambiciosos de Oriente Medio.✨ Contrato Indefinido.✨ Modelo presencial en Abu Dhabi desde las oficinas del cliente. Opción teletrabajo un día a la semana.✨ Alojamiento en hotel de cinco estrellas durante toda la estancia.✨ 100 € diarios para manutención.✨ Cuenta corporativa de Uber y tarjeta de gastos.✨ Un vuelo de ida y vuelta a España cada 4-6 semanas (o vuelo equivalente para tu pareja a Abu Dhabi).✨ Bonus mensual de desplazamiento de 1.500 €.✨ Seguro médico internacional premium.✨ Posibilidad de extensión del proyecto hasta agosto de 2027.✨ 500€ anuales en formación, asignados explícitamente a necesidades formativas del empleado.✨ Tarjeta regalo de 100€ de Amazon en Navidad.✨ 23 días de vacaciones al año.

MLOps Engineer - Abu Dhabi, UAEDiscover the OpportunityWe’re partnering with a leading financial services organisation in Abu Dhabi that is investing significantly in its AI and data capabilities.Discover the roleWe are seeking an experienced MLOps Engineer to join its AI innovation group. In this role, you will design and develop an enterprise-grade ML platform that empowers data science teams to accelerate model experimentation, streamline deployment, and scale AI solutions across the organization.Design and automate end-to-end ML pipelines for continuous integration, continuous delivery, and continuous training (CI/CD/CT).Configure model serving infrastructure supporting both ultra-low latency real-time APIs (e.g., FastAPI, gRPC) and large-scale batch inference.Implement production monitoring and logging systems to track model performance, runtime latencies, data drift, and concept decay.Manage model registries and metadata using tools like MLflow or Kubeflow to ensure auditable lineage, reproducible versioning, and artifact tracking.Orchestrate and scale containerized applications using Docker and production-grade Kubernetes or managed cloud services (EKS, AKS).Partner with security and data governance teams to enforce pipeline encryption, network isolation, secure IAM policies, and strict compliance standards.Discover the Requirements3–7 years of hands-on experience building and operating high-availability ML pipelines, automation scripts, and foundational platform infrastructure.Strong programming proficiency in Python, Go, or Java, combined with systems scripting and deep Linux environment expertise.Deep familiarity with tools such as MLflow, Kubeflow, Argo Workflows, Feast, or managed platform equivalents (SageMaker, Azure ML).Hands-on experience with Docker, Kubernetes, Infrastructure-as-Code (Terraform, CloudFormation), and cloud security (VPC isolation, private endpoints, RBAC, encryption).Bachelor’s degree in Computer Science, Engineering, or a related field required; Master’s degree preferred.Why Join?An excellent salary package on offerEnjoy a 4.5-day work week, plus 24 annual flexi-work days allowing you to work from anywhere.Full platinum health cover for you and your sponsored dependents.You will be a part of one of the region's biggest AI innovation teams and gain the opportunity to work with cutting-edge technologies to transform large-scale enterprise banking systems.

Machine Learning Engineer This position is on site in Abu Dhabi, relocation is required. In this position you will design and develop scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision-making processes.Key Responsibilities• Work with large and complex data sets to solve challenging business problems.• Efficient training and deployment of standard ML, NN, and Agentic models.• Develop, train, and optimize machine learning models using state-of-the-art algorithms andframeworks.• Build production grade end-to-end ML pipelines, including data ingestion, transformation,model training, validation, and deployment.• Automate workflows for model training, testing, and deployment using CI/CD pipelines andMLOps tools.• Collaborate with cross-functional teams to integrate models into applications and deliverend-to-end solutions.• Finetune SLMs/LLMs and build complex AI architectures.Qualifications / Experience• (3-7) years of experience in building production grade, scalable AI systems.• Expert in supervised/unsupervised learning, deep learning, NLP, computer vision, orgenerative AI (e.g., LLMs).• Strong ML system architecture skills.• Understanding of model serving, API development (FastAPI, Flask), and optimizing modelperformance for real-time or batch inference.• General Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools like MLflow/Kubeflowfor model lifecycle management (MLOps).• Comfortable with deploying models on AWS or Azure.• Minimum Educational qualifications: Bachelor’s degree in Computer Science, Engineering,or related field required.• Preferred Education qualifications: Master or PhD in Computer Science or a related field

About CompanyOur client is a semi-government entityWe are looking for AI Engineers who can build end-to-end AI solutions & agents.Candidates who have prior experience of working with consulting organizations and have experience in translating business requirements into technical solutions are preferred.Technical SkillsRequire candidates with full-stack capability (front end and back end development)Experience designing scalable system architecturesFamiliarity with cloud platforms, APIs and data pipelinesThis role requires the ability to manage full development lifecycle - from prototyping to deployment.Preference will be given to candidates based in the UAE currently.Perks And BenefitsApplicable as per labour law provisionsSkills: ai agents,full stack development,web based applications,system architecture

Senior Data Scientist – AI Research LabWe are building the next generation of artificial intelligence systems, bringing together researchers, engineers and product teams to solve complex real-world problems using state-of-the-art machine learning. Our work spans large language models (LLMs), multimodal AI, agentic systems, reinforcement learning, computer vision and advanced predictive modelling.This is an opportunity to work in a research-led environment where experimentation, innovation and scientific thinking are valued as highly as engineering excellence.The RoleWe are looking for an exceptional Data Scientist to transform large, complex datasets into insights, models and algorithms that power cutting-edge AI products and research initiatives.You'll work alongside AI Researchers, Machine Learning Engineers and Software Engineers to develop novel approaches, evaluate model performance and build intelligent systems that solve meaningful business and scientific challenges.This role suits someone who enjoys working at the intersection of statistics, machine learning and applied AI.ResponsibilitiesDesign, build and deploy advanced statistical and machine learning models.Analyse large-scale structured and unstructured datasets to uncover meaningful insights.Develop predictive models using classical ML techniques and modern deep learning approaches.Partner with AI Researchers to evaluate foundation models, LLMs and multimodal systems.Build data pipelines, feature engineering frameworks and model evaluation methodologies.Design experiments and A/B testing frameworks to validate model improvements.Develop explainability, bias detection and model monitoring capabilities.Evaluate new research papers and translate academic advancements into production-ready solutions.Work with cloud-native data platforms and distributed computing environments.Present technical findings to both technical and executive stakeholders.Required ExperienceBachelor's, Master's or PhD in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence or a related discipline.5+ years of commercial experience in Data Science or Machine Learning.Strong mathematical foundation including probability, statistics and optimisation.Expert Python programming skills.Experience with SQL and large-scale data processing.Strong experience with Scikit-learn, Pandas, NumPy and modern ML frameworks.Experience working with PyTorch or TensorFlow.Knowledge of feature engineering, model selection and hyperparameter optimisation.Experience deploying models into production environments.Strong understanding of evaluation metrics, experimentation and statistical significance.Excellent communication and problem-solving skills.Desirable ExperienceExperience working with Large Language Models (GPT, Llama, Claude, Mistral or similar).Knowledge of Retrieval-Augmented Generation (RAG).Experience with reinforcement learning or agentic AI systems.Familiarity with vector databases and embedding models.Experience with distributed computing (Spark, Ray or Dask).Knowledge of MLOps tools including MLflow, Kubeflow or SageMaker.Experience using Kubernetes and Docker.Exposure to computer vision, speech or multimodal AI.Publications, patents or open-source contributions within AI or machine learning.40/50k AED per month

At Xebia, we're building AI capability from the ground up. We don't buy off-the-shelf solutions. We design, build, deploy, and run AI systems ourselves.As a Forward Deployed AI Engineer, you'll be embedded directly within the business, working closely with stakeholders to understand not just the problem, but the "why" behind it. You'll take ownership of real aviation outcomes, designing and delivering enterprise-grade Agentic AI solutions from concept to production.Working alongside a Business Product Owner and an AI Value Architect, you'll leverage Xebia's AI platform, MCP fabric, and engineering standards to rapidly build scalable, secure, and impactful AI applications.Accountabilities & ResponsibilitiesUnderstand before you build. Start every problem with the business need and the why, working directly with stakeholders, then take the solution from discovery to production.Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working.Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale.Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces.Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails.Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost.Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment.Take single-threaded ownership of a domain outcome (one owner, one result), and help establish reusable patterns that grow Customer's internal AI capability rather than renting it.Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool.Education & ExperienceWe look for a hands-on Core-level engineer who combines a business-first mindset with real agentic-AI engineering depth:Curiosity above all: you dig into problems, question assumptions and want to understand how the airline actually works.A business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team.Around 3 years building production-grade software with GenAI and LLMs, including about 1 year of hands-on agentic AI: applications that go beyond prompting or basic chatbots, with tool calling, workflow orchestration, RAG, context management, evaluation and monitoring.Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs and data.Strong Python, with basic knowledge of at least one of TypeScript / JavaScript, and modern engineering practice: async programming, FastAPI, Pydantic, Git and CI/CD, testing, error handling and logging.Practical experience with at least one agent framework or enterprise AI platform (e.g. LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, Google Vertex/Gemini) and with a vector database or search platform (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch).Experience integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud with containers.Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering).Bachelor’s degree in computer science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.Growth path: grow into Senior Forward Deployed AI Engineer and Technical Lead, and onward to AI Value Architect, owning a cluster’s value journey while still building.

Job SummaryAceNet Consulting is looking for a highly skilled Senior AI Solutions Engineer with 6–8 years of experience in Artificial Intelligence, Machine Learning, and Solution Architecture. The ideal candidate will be responsible for designing, developing, and deploying enterprise-grade AI solutions that address complex business challenges. You will work closely with cross-functional teams to build scalable, secure, and high-performing AI applications while ensuring adherence to architecture governance, coding standards, and best practices.Key ResponsibilitiesDesign, architect, and implement scalable AI and Machine Learning solutions for enterprise applications.Develop AI-powered applications using modern AI frameworks and cloud technologies.Build and integrate AI solutions with enterprise platforms, data lakes, APIs, and business applications.Collaborate with business stakeholders, architects, data engineers, and development teams to understand requirements and deliver innovative AI solutions.Ensure solution scalability, security, reliability, and performance through architecture governance and technical best practices.Design and implement data pipelines supporting AI and machine learning workloads.Integrate AI models with enterprise systems using REST APIs and middleware technologies.Leverage cloud platforms, containerization, and DevOps practices for AI solution deployment.Mentor technical teams and contribute to architecture reviews, code quality, and engineering standards.Stay updated with emerging AI technologies and recommend innovative approaches for business transformation.Required SkillsBachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related field.6–8 years of experience in AI, Machine Learning, and Solution Architecture.Strong programming expertise in Python, Java, or C++.Hands-on experience with Microsoft Azure and AI cloud services.Experience with AI frameworks and platforms including OpenAI APIs, LangChain, Hugging Face, and Foundry.Strong understanding of Machine Learning algorithms, Deep Learning, data processing, and enterprise AI solutions.Experience with cloud infrastructure, Docker, Kubernetes, and DevOps practices.Knowledge of data architecture, ETL pipelines, Microsoft Fabric/Data Lakehouse concepts, and enterprise data integration.Experience integrating AI solutions with Salesforce CRM, CMS platforms, data lakes, and RESTful APIs.Strong understanding of AI security, governance, privacy, and architecture best practices.Excellent analytical, problem-solving, communication, and stakeholder management skills.Good to HaveExperience with Reinforcement Learning, Natural Language Processing (NLP), or Computer Vision.Knowledge of AI ethics, responsible AI, and bias mitigation strategies.Experience working in enterprise-scale digital transformation initiatives.Leadership experience mentoring technical teams and driving architectural decisions.

Exciting Opportunity Alert! 🌟 HTC Global Services is hiring AI Architect for an 1-year extendable contract in Abu Dhabi, UAE (Onsite).HTC Global Services - a leading CMM level 5 global provider of innovative IT and Business Process Services and Solutions since 1990 with headquarters in Troy, Michigan, USA.Technical Skills & ExpertiseAgentic AI & Generative AI: Strong hands-on experience designing, building and deploying production-grade Agentic AI applications, including autonomous and multi-agent workflows, AI agents, planning and reasoning patterns, memory, tool usage and human-in-the-loop systems.Large Language Models (LLMs): Deep understanding of LLM capabilities, limitations and implementation patterns, including prompt engineering, structured outputs, function/tool calling, context management and model selection.LLM Orchestration: Experience with LLM orchestration frameworks and patterns such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI or similar technologies.Retrieval-Augmented Generation (RAG): Hands-on experience designing and optimizing RAG pipelines, including document ingestion, chunking strategies, embeddings, vector search, retrieval, reranking, context optimization and grounded response generation.Vector Databases & Knowledge Retrieval: Experience working with vector databases and search technologies such as Azure AI Search, Pinecone, Weaviate, Milvus, pgvector, FAISS or similar platforms.Model Context Protocol (MCP): Strong understanding and practical experience with MCP architecture, including building and integrating MCP servers, tools, resources and AI-agent integrations.AI Tool Integration: Experience integrating AI agents with enterprise applications, APIs, databases and external tools using REST APIs, GraphQL, SDKs, function calling and event-driven architectures.AI Guardrails & Responsible AI: Experience implementing AI safety and governance mechanisms, including guardrails, content filtering, prompt injection protection, PII protection, access controls, human approval workflows and responsible AI practices.AI Evaluation & Observability: Strong experience defining and implementing evaluation frameworks for AI applications, including automated and human evaluation, benchmarking, hallucination detection, quality metrics, tracing, monitoring and feedback loops.AI Performance & Cost Optimization: Ability to evaluate and optimize trade-offs between latency, quality, reliability, scalability and cost, including model selection, caching, token optimization and efficient AI architecture design.Python & Software Engineering: Strong hands-on programming expertise in Python, with experience building scalable, production-grade applications. Experience with FastAPI or similar backend frameworks is highly desirable.Cloud AI Platforms: Hands-on experience with one or more cloud AI ecosystems, preferably Microsoft Azure, including services such as Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Databricks or equivalent AWS/GCP AI services.Data Engineering & Integration: Good understanding of enterprise data architecture, including structured and unstructured data, APIs, databases, data pipelines, streaming/event-driven systems and knowledge ingestion pipelines.MLOps / LLMOps: Experience with CI/CD, model and prompt versioning, deployment automation, experimentation, monitoring, observability and lifecycle management for AI and ML applications.Containers & Deployment: Experience with Docker, Kubernetes and modern cloud-native deployment practices.Security & Enterprise Architecture: Understanding of enterprise security principles, including authentication, authorization, secrets management, API security, data privacy and secure AI application design.Architecture & Technical Leadership: Ability to define end-to-end AI solution architecture, establish engineering standards, conduct code and architecture reviews, and guide teams on reusable patterns and technical best practices.Technical Experience Required8+ years of experience in software engineering, AI/ML, data engineering or related technical domains.Strong hands-on experience building and deploying Generative AI, LLM and Agentic AI solutions into production.Proven experience with LLM orchestration, RAG, tool calling, MCP integrations, vector databases, guardrails and AI evaluation frameworks.Strong proficiency in Python and modern software engineering practices.Experience designing scalable, secure and reliable enterprise AI architectures.Hands-on experience leading technical teams while remaining actively involved in coding, solution design, code reviews and architecture decisions.Experience defining AI application KPIs, evaluation metrics, feedback loops and production monitoring.Ability to make technical decisions based on business value, feasibility, security, scalability, latency, quality and cost.Interested candidates please do share your updated CV to mubeenakamal.basha@htcinc.com mentioning your Current CTC, expected CTC and notice period details.#HTCGlobalServices #ITJobs #hiring #lookingforjob #careers #jobs #immediatejoiner #recruitment #technology #jobseekers #interview #lookingforjobchange #newjob #AIarchitect #LLM #RAG #MCP #fastMCP #Python #GenAI #AgenticAI #GenerativeAI

Responsibilities:Understand the business need before building and take solutions from discovery through production.Design, build, deploy and continuously improve enterprise-grade agentic AI applications.Build agents that reason across multiple steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop workflows.Design and implement RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.Build MCP-based integrations and connect agents to backend systems through REST/OpenAPI, webhooks and event-driven patterns with secure authentication.Apply structured LLM patterns including tool calling, schema-validated outputs, retries, fallbacks and guardrails.Coordinate with Business Product Owners, AI Value Architects and other squads.Own end-to-end delivery of significant AI use cases and set the pace on quality, evaluation and reliability.Requirements:Degree in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience.5+ years building production-grade software, including 3+ years with GenAI and LLMs.Minimum 1 year of hands-on Agentic AI experience with a proven record of taking agentic solutions to production.Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs and data.Strong Python with working knowledge of TypeScript / JavaScript. Java or C# is a plus.Experience with async programming, FastAPI, Pydantic, Git, CI/CD, testing, error handling and logging.Experience with at least one agent framework or enterprise AI platform such as LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore or Google Vertex/Gemini.Experience with vector databases/search platforms such as Azure AI Search, pgvector, Pinecone, Weaviate or OpenSearch.

We are seeking an experienced Lead AI Engineer to lead the development and deployment of scalable AI and Generative AI solutions while managing a growing AI Engineering team.This role combines technical leadership, people management, and hands-on engineering. The successful candidate will be responsible for driving AI initiatives from concept and prototyping through to production deployment.Key ResponsibilitiesLead, mentor, and develop a team of AI and Engineering professionals.Drive end-to-end delivery of AI and Generative AI solutions.Design and deploy LLM, RAG, AI Agent, and multimodal AI applications.Establish best practices across MLOps, CI/CD, testing, deployment, and monitoring.Conduct technical reviews and ensure high standards of software engineering and architecture.Collaborate with Product, Engineering, and business stakeholders to deliver impactful AI solutions.Champion responsible AI, security, privacy, and governance standards.Requirements8-10+ years of experience in AI/ML Engineering or Software Engineering.Strong people management and team leadership experience.Strong foundation in Machine Learning and Natural Language Processing developed before the GenAI era.Experience building and deploying production-scale AI applications.Advanced Python development skills.Strong understanding of MLOps, CI/CD, cloud deployment, and software engineering best practices.Hands-on experience with Generative AI technologies including LLMs, RAG, AI Agents, and evaluation frameworks.Experience delivering scalable AI products in production environments.Excellent communication and stakeholder management skills.Preferred SkillsAzure OpenAIAzure AI SearchAzure Machine LearningAzure AI FoundryDocker, Kubernetes, GitHub ActionsExperience within EdTech or digital product environmentsArabic language skillsIdeal CandidateWe're particularly interested in candidates who possess deep AI/ML expertise predating the recent GenAI boom, combined with hands-on experience delivering modern LLM and Generative AI solutions at scale. Candidates should be comfortable leading teams while remaining technically involved in architecture, design, and implementation.

AI Applications EngineerThe AI Applications Engineer will build, own, and operate Euro Mechanical's AI and application platform on Microsoft Azure. This role establishes a secure, governed, company-owned foundation for AI, automation, document-processing, analytics, and back-office solutions across the group — working closely with business units, IT, and delivery partners to simplify processes, reduce manual effort, and improve efficiency.The role covers the full lifecycle: design, development, deployment, monitoring, maintenance, and continuous improvement.ResponsibilitiesBuild and maintain the platform on Azure — data layer, model access, integrations, and supporting infrastructure — in line with agreed architecture, security, and governance requirementsOwn the platform post go-live: reliability, performance, security, scalability, and costWork with delivery partners during implementation to ensure knowledge, code, and expertise transfer to Euro MechanicalPartner with business units to identify AI, automation, and process-improvement opportunities, and translate these into practical solutionsDevelop AI assistants, copilots, document-processing tools, and back-office automation using Azure AI Foundry, Microsoft Copilot, and other approved technologiesDesign and optimise Retrieval-Augmented Generation solutions (embeddings, retrieval, prompt engineering)Build prototypes through to production-ready applications, and deliver solutions such as sales inbox automation, AP/AR automation, bid-generation support, and profitability analyticsIntegrate solutions with Dynamics 365 F&O, Microsoft 365, SharePoint, and Power Platform, including APIs and data pipelinesMonitor platform health, troubleshoot issues, and manage cloud cost and performanceMaintain technical documentation and ensure solutions are secure, maintainable, and not overly vendor-dependentWork within Euro Mechanical's AI and security policies, and act as a technical point of contact for stakeholders across the businessRequirementsBachelor's degree in Computer Science, Software Engineering, AI, Data Science, or related field5+ years' hands-on experience in cloud engineering, software development, AI, data engineering, or automationProven experience owning a platform or production solution end-to-endStrong hands-on Azure experience (compute, storage, networking, security, monitoring, governance)Practical experience with Azure AI Foundry, Copilot, or similar enterprise AI servicesStrong knowledge of generative AI: agents, RAG, embeddings, vector search, prompt engineeringStrong API development, systems integration, Python and SQL proficiencyExperience integrating with Dynamics 365 (F&O preferred), Microsoft 365, SharePoint, and Power PlatformSolid understanding of application securityStrong documentation, problem-solving, and stakeholder communication skillsAzure certifications (Developer, Data Engineer, or AI Engineer Associate) advantageousExperience with agentic workflows, RPA, or transferring capability from a delivery partner is a plusTo express interest, please apply online, and we will contact suitable candidates shortly.

European candidates are highly encouraged to apply(UK, Germany, Switzerland, Netherlands, Belgium, Ireland), from Australia and Hong Kong.Based in Abu Dhabi, UAEAED 60 000 - 70,000 per month (Tax Free) + Outstanding Benefits + Hybrid Working + Relocation PackageMUST COME FROM CONSULTING ENVIRONMENT!We're partnering with one of the world's leading global technology consulting and digital transformation organisations, delivering cutting-edge AI, Cloud and Enterprise Technology solutions to some of the world's largest businesses. This is an opportunity to join an elite engineering team driving next-generation AI innovation across enterprise-scale platforms.If you're passionate about Generative AI, Machine Learning, MLOps and enterprise engineering, this could be the career move you've been waiting for.The OpportunityAs a Principal AI/ML Engineer, you'll provide technical leadership across enterprise AI initiatives, leading the design, architecture and delivery of production-grade AI and Machine Learning platforms. You'll work alongside architects, cloud engineers, software engineers and senior business stakeholders to build scalable, secure and responsible AI solutions.Key Requirements✔ Extensive experience delivering AI/ML solutions into production✔ Expert Python development✔ Strong experience with PyTorch, TensorFlow and Scikit-learn✔ Deep understanding of Machine Learning, Deep Learning and Generative AI✔ Strong MLOps exp including CI/CD, model deployment, monitoring and lifecycle management✔ Cloud experience across Azure, AWS or GCP✔ Proven technical leadership, mentoring and architecture ownership✔ Experience building scalable enterprise AI platforms and engineering best practicesWhat's on offer?💰 Salary TAX FREE✈️ Relocation package worth approximately AED 50,000 (£10,000 GBP)🏥 Comprehensive medical insurance🌴 Generous annual leave🏡 Hybrid working model🚀 Career progression within a global technology leader🌍 Relocation to vibrant Abu Dhabi with an exceptional quality of life

SENIOR AI ENGINEER – Agentic AI & Enterprise AI Platforms:🎯 Essential: 5+yrs Software/AI engineering + building production + Tech/AI start-up💰 Compensation: $200,000 – $400,000 Total Compensation📍 Location: Abu Dhabi, UAE 🇦🇪 (relocation & visa supported)The OpportunityWe're partnering with a highly successful global hedge-fund that is making a significant long-term investment in AI engineering. We're looking for a hands-on AI Engineer / Software Engineer (Tech/AI company preferred over Finanical Services) to join a small, high-calibre team building a new AI product from the ground up.This is not an AI research, consulting or strategy role. It's a highly technical engineering role focused on building, shipping and operating production-grade agentic AI systems, with significant scope for experimentation and product development.You'll work with modern LLM technologies to build agent orchestration, tool use, RAG/GraphRAG, context and memory systems, evaluation and observability, alongside the underlying APIs, services and deployment infrastructure required to run them reliably in production.The environment has the pace, autonomy and experimentation of an AI startup, combined with the resources and scale to take what you build into production.What You'll DoDesign, build and ship production-grade AI applications and agentic systems.Build agent orchestration incorporating planning, tool use, context and memory.Develop RAG and GraphRAG systems, semantic search and knowledge retrieval.Build reliable LLM-powered workflows and AI products, taking ideas from experimentation through to production.Develop the underlying APIs, services and infrastructure required to operate AI systems at scale.Build evaluation frameworks, observability and monitoring to measure and improve agent performance.Work with containers, cloud infrastructure and deployment/control-plane technologies to run AI systems reliably.Experiment rapidly with emerging models, frameworks and architectures and turn successful ideas into production capabilities.Take genuine ownership of systems from architecture through implementation, deployment and operation.What We're Looking ForWe're looking for software engineers who are exceptional AI engineers — people who genuinely enjoy building things and are comfortable operating at the cutting edge of the technology.You'll ideally have:Strong software engineering fundamentals and significant hands-on coding experience.Proven experience shipping and operating production AI/agentic systems.Strong Python experience and a track record of building APIs, services and production software.Hands-on experience with agent orchestration, tool calling, RAG/GraphRAG, context or memory management.Experience building evaluation, observability, monitoring or reliability systems for AI applications.Experience with modern LLM/agent frameworks such as LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Claude Agent SDK, CrewAI or similar.Experience with cloud infrastructure and modern deployment practices, ideally including Docker, Kubernetes or equivalent containerised environments.A strong interest in AI product experimentation and the ability to take an idea from prototype to something people actually use.The ability to work autonomously in a small, highly technical engineering team.We're particularly interested in engineers coming from:AI-native technology companiesEarly-stage / high-growth AI startupsTeams building production agentic AI productsAI platform / infrastructure engineering teamsStrong software engineering teams where you've moved deeply into LLM and agent engineeringWe're less concerned with the prestige of the company on your CV than what you personally built and shipped. Financial-services experience is not required.Why This Role?Build a new AI product from the ground up, rather than maintaining an established platform.Work with a small team of highly technical AI/software engineers.Significant technical ownership and autonomy.Work directly with the latest developments in agentic AI and LLM engineering.Experiment rapidly and see your ideas move from prototype to production.Access to the resources and scale of a world-class investment firm without the bureaucracy of a traditional enterprise environment.Highly competitive compensation.Opportunity to work alongside senior AI leadership and help define how the next generation of AI products are engineered.If this isn’t for you but you know someone who’d be interested, then why not confidentially recommend them by clicking here http://peartalent.co.uk/spread-the-word/. We have a fantastic referral scheme up to £1,000*.

Hi,#Xebia is looking for Forward Deployed AI EngineerLocation- Abu DhabiExp- 4+ yearsPurposeXebia is building in-house AI expertise to deliver AI in aviation. We build, we do not buy off the shelf. As a Forward Deployed AI Engineer you are the core of a Squad, you sit with stakeholders to understand the problem and the why, then design, build, deploy and run the agentic AI systems that solve it, end to end. This is a hands-on, build-first role with single-threaded ownership of a real aviation outcome, working alongside a Business Product Owner and an AI Value Architect, on a shared platform (paved road, MCP fabric, standards) that lets your squad self-serve against systems. Accountabilities & Responsibilities:Understand before you build. Start every problem with the business need and the why, working directly with stakeholders, then take the solution from discovery to production.Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working.Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale.Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces.Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails.Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost.Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment.Take single-threaded ownership of a domain outcome (one owner, one result), and help establish reusable patterns that grow internal AI capability rather than renting it.Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool.Education & Experience:We look for a hands-on Core-level engineer who combines a business-first mindset with real agentic-AI engineering depth:Curiosity above all: you dig into problems, question assumptions and want to understand how the airline actually works.A business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team.Around 3 years building production-grade software with GenAI and LLMs, including about 1 year of hands-on agentic AI: applications that go beyond prompting or basic chatbots, with tool calling, workflow orchestration, RAG, context management, evaluation and monitoring.Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs and data.Strong Python, with basic knowledge of at least one of TypeScript / JavaScript, and modern engineering practice: async programming, FastAPI, Pydantic, Git and CI/CD, testing, error handling and logging.Practical experience with at least one agent framework or enterprise AI platform (e.g. LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, Google Vertex/Gemini) and with a vector database or search platform (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch).Experience integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud with containers.Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering).Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.Growth path: grow into Senior Forward Deployed AI Engineer and Technical Lead, and onward to AI Value Architect, owning a cluster’s value journey while still building.

🚀 AI Forward Deployed Engineer | Generative AI | Abu Dhabi 🇦🇪💰 AED 40,000 - 50,000 per month TAX FREE + Outstanding Benefits + Hybrid Working + £10,000 Relocation PackageMUST COME FROM CONSULTING ENVIRONMENT!Relocation from Europe, UK, Australia & Hong Kong welcomed!We're particularly interested in candidates currently based in:🇬🇧 United Kingdom🇩🇪 Germany🇨🇭 Switzerland🇳🇱 Netherlands🇧🇪 Belgium🇮🇪 Ireland🇦🇺 Australia🇭🇰 Hong KongAre you ready to work at the forefront of Enterprise AI?We're partnering with a globally recognised technology leader delivering AI, cloud and digital transformation solutions to some of the world's largest organisations. This is a rare opportunity to become one of the founding engineers within an elite AI team building next-generation Generative AI solutions for enterprise customers across the Middle East.You'll work directly with C-level stakeholders and major global organisations, designing and deploying cutting-edge AI solutions that solve real business problems using the latest LLMs, Agentic AI and Machine Learning technologies.What You'll Be DoingDesign and deploy enterprise-grade Generative AI solutionsBuild AI applications, LLM workflows and Agentic AI systemsWork directly with strategic enterprise customersPrototype innovative AI solutions from concept through productionCollaborate with Sales, Product and AI specialists on high-impact engagementsHelp shape the future of enterprise AI adoptionWhat We're Looking For✔ 4+ years Software Engineering & Applied AI development ✔ Strong Python or JavaScript development✔ Hands-on experience with Generative AI, LLMs, GPT, Agentic AI or AI applications✔ Cloud experience (Azure or AWS)✔ Experience deploying AI/ML solutions into production✔ Customer-facing consulting experience & come from client-facing role✔ Excellent communication and stakeholder management skills✔ Comfortable translating business challenges into technical AI solutions✔ Entrepreneurial mindset with a passion for innovationWhy You'll Love It💰 AED 50,000 per month TAX FREE✈ £10,000 Sterling Relocation Package🏡 Hybrid Working🏥 Excellent Benefits🌍 International Team🚀 Work with cutting-edge AI technologies before they become mainstream☀️ Live in Abu Dhabi - one of the safest and fastest-growing technology hubs in the world

🚀 Hiring: AI Value Architect | Agentic AI | Abu Dhabi, UAEWe are looking for a hands-on AI Value Architect / Agentic AI Technical Lead to build and lead enterprise-grade GenAI and Agentic AI solutions for complex real-world business scenarios.📍 Location: Abu Dhabi, UAE – 5 Days Onsite💼 Employment: Full-Time🧠 Experience: 8+ Years⏳ Notice Period: Immediate / Serving Notice / Maximum 30 Days🛂 Valid Passport Mandatory✈️ Candidates currently outside UAE can also apply if willing to relocate to Abu Dhabi.🔥 Mandatory Technical Skills✅ RAG – Retrieval Augmented Generation✅ Large Language Models – LLM✅ MCP – Model Context Protocol✅ Python & FastAPI✅ LangChain✅ LangGraph✅ Agentic AI / AI Agents🎯 What We’re Looking ForCandidates with strong hands-on experience in:• Building and deploying production-grade Agentic AI applications• Designing RAG pipelines using chunking, embeddings, vector search and retrieval optimization• Developing LangGraph-based AI agents, tool calling and multi-step workflows• Building and integrating MCP servers/clients with enterprise tools, APIs and data sources• Developing scalable GenAI APIs using Python, FastAPI and Pydantic• Working with LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama or similar• Vector databases/search platforms such as Azure AI Search, Pinecone, pgvector, Weaviate or OpenSearch• Multi-agent systems, human-in-the-loop workflows and API/tool orchestration• Cloud deployment, Docker/Kubernetes, CI/CD, monitoring and observability• LLM/RAG evaluation, guardrails, security and responsible AI⭐ Experience in Airline, Aviation, would be a strong advantage.This is an excellent opportunity for someone who is still hands-on with coding while capable of setting architecture and technical direction for Agentic AI solutions.📩 Interested candidates can DM me or share their updated CV along with:• Total Experience• GenAI / LLM Experience• RAG Experience• Agentic AI Experience• MCP Experience• LangChain / LangGraph Experience• FastAPI Experience• Current Location• Notice Period / LWD• Current & Expected Compensation• Passport Status• Willingness to relocate to Abu Dhabi🔁 Please like, comment or share with relevant GenAI / Agentic AI professionals in your network.#Hiring #AIJobs #AgenticAI #GenerativeAI #GenAI #ArtificialIntelligence #AIArchitect #AIValueArchitect #GenAIArchitect #AgenticAIArchitect #AIEngineer #LeadAIEngineer #PrincipalAIEngineer #LLM #LLMEngineer #LargeLanguageModels #RAG #RetrievalAugmentedGeneration #MCP #ModelContextProtocol #LangChain #LangGraph #FastAPI #Python #PythonDeveloper #AIAgents #MultiAgentSystems #AIEngineering #AppliedAI #MachineLearning #DeepLearning #PromptEngineering #VectorDatabase #AzureOpenAI #OpenAI #AzureAI #AWSBedrock #GoogleVertexAI #Pinecone #AzureAISearch #Docker #Kubernetes #MLOps #LLMOps #AIOps #UAEJobs #AbuDhabiJobs #DubaiJobs #MiddleEastJobs #TechJobs #ITJobs #OnsiteJobs #RelocationJobs #AviationJobs #AirlineJobs #HiringNow #ImmediateJoiners #TechHiring #Recruitment

This is a company focused on developing AI-driven solutions for defense and infrastructure, specializing in cutting-edge machine learning models for supply chain optimization, compliance automation, and predictive analytics while adhering to defense-grade security standards. Its transformative platforms combine generative AI with operational workflows to enhance efficiency, risk management, and decision-making in high-stakes industries.Job SummaryThis company, is seeking a Senior Machine Learning Engineer to spearhead the development and implementation of cutting-edge AI solutions for high-impact initiatives. In this pivotal role, you will oversee the complete lifecycle of machine learning systems—from conceptual design and data preparation to model training, optimization, and secure deployment in production environments. You will drive innovation at the intersection of generative AI and traditional machine learning, focusing on two transformative projects: one being, an automated requirements engineering platform leveraging large language models (LLMs) for regulatory compliance and rule extraction, and the Intelligent Supply Chain, which integrates predictive analytics for demand forecasting, risk assessment, and procurement optimization. Operating within a structured agile framework—from Sprint Zero to Stage Gate—you will ensure that all models meet rigorous standards for accuracy, robustness, explainability, and defense-grade security. This role demands a blend of technical expertise, domain adaptability, and collaborative leadership. You will work closely with cross-functional teams, including data scientists, backend engineers, and domain experts, to align technical solutions with business objectives while adhering to strict engineering best practices and documentation requirements.Key ResponsibilitiesLead the design and implementation of Large Language Model (LLM) pipelines, including parsing complex regulatory texts (e.g., military standards, building codes) to extract structured rules and formalize natural language requirements into executable logic tuples.Develop and optimize Retrieval-Augmented Generation (RAG) architectures to enable semantic search and querying of technical documentation and historical project data for compliance and decision-making purposes.Engineer advanced prompt strategies (e.g., few-shot learning, chain-of-thought) to enhance model performance on domain-specific tasks while minimizing the need for extensive retraining.Build time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals for supply chain optimization.Design classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.Create multi-objective optimization algorithms to balance conflicting priorities such as cost, lead time, and risk in procurement decision-making processes.Containerize machine learning models using Docker and Kubernetes, deploying them into secure, on-premise inference environments adhering to defense-grade security standards.Construct automated training and inference pipelines with tools like Kubeflow or MLflow to ensure reproducibility, scalability, and compliance with engineering best practices.Optimize model inference latency and resource efficiency through techniques such as quantization and distillation, ensuring seamless operation on available hardware.Implement comprehensive monitoring systems to track model drift and performance degradation in production, establishing feedback loops for continuous improvement and retraining.Professional QualificationsFive or more years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.Ability to quickly learn and apply machine learning techniques to specialized domains such as defense engineering, supply chain, or construction.Experience working in agile environments using Sprints while adhering to rigorous engineering standards and documentation requirements.Strong communication skills to collaborate effectively with Data Scientists, Backend Engineers, and Domain Experts, ensuring technical solutions align with business needs.Required Technical SkillsExpert proficiency in Python and standard machine learning libraries including PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.Strong experience with transformer architectures (BERT, GPT, Llama) and natural language processing frameworks such as Hugging Face and LangChain.Proficiency in MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).Ability to design and implement data preprocessing pipelines for both structured data (SQL, tabular formats) and unstructured data (text, PDFs).Deep understanding of algorithmic principles for custom logic implementation, including graph traversal and geometric computations.Experience with Retrieval-Augmented Generation (RAG) architectures and advanced prompt engineering techniques (few-shot learning, chain-of-thought).Familiarity with time-series forecasting models, classification algorithms, and anomaly detection techniques for predictive analytics.Knowledge of model optimization strategies such as quantization, distillation, and latency reduction for production deployment.Hands-on experience with model deployment frameworks, including containerization and secure on-premise inference environments.Ability to build and maintain automated training and inference pipelines to ensure reproducibility and scalability in production.Work LocationOnsite Location: Abu Dhabi, UAERemote:Project Focus: Intelligent Supply Chain & LeverEDGE PlatformRole OverviewAs a Senior Machine Learning Engineer, you will lead the development and deployment of advanced AI models, covering the full lifecycle from architectural design to production deployment. This role focuses on integrating generative AI with traditional machine learning to power two core initiatives: an automated requirements engineering platform leveraging LLMs, and Intelligent Supply Chain, which delivers predictive risk scoring and demand forecasting. You will operate within a structured delivery framework—from Sprint Zero to Stage Gate—to ensure models are accurate, robust, explainable, and deployable in defense-grade security environments.Automated Requirements EngineeringDesign and optimize LLM and NLP pipelines to parse complex regulatory texts (e.g., military standards, building codes) and extract structured rules. Convert natural language requirements into executable formats (e.g., logic tuples) for downstream compliance systems. Implement Retrieval-Augmented Generation (RAG) architectures to enable semantic search across technical documentation and historical project data. Enhance model performance through advanced prompt engineering techniques, including few-shot learning and chain-of-thought strategies, tailored to domain-specific tasks without extensive retraining.Intelligent Supply Chain: Predictive Analytics & Risk ManagementDevelop time-series forecasting models to predict material demand and spend, integrating ERP data with external market signals. Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors. Design multi-objective optimization algorithms to balance cost, lead time, and risk for procurement decision-making.MLOps & Production DeploymentContainerize models using Docker and Kubernetes for secure, on-premise deployment. Orchestrate automated training and inference pipelines with tools like Kubeflow or MLflow to ensure reproducibility and scalability. Optimize model inference latency and resource efficiency through techniques such as quantization and distillation. Implement monitoring systems to track model drift and performance, establishing feedback loops for continuous improvement.

Lead AI EngineerTelco & MediaAbu Dhabi, United Arab EmiratesPermanent roleThis role requires you to work onsite in Abu Dhabi. We are looking for a Lead AI Engineer for a media company in Abu Dhabi. You will have an opportunity to work in business with complete buy-in from C-Level stakeholders to become AI-first.Experience needed:8+ years in industry (AI/Engineering)Own delivery of enterprise AI deliverablesDevelop AI InfrastructureBuild and deploy AI agents and internal toolsEvaluate and integrate emerging AI models, APIs, and infrastructure componentsIdentify patterns where AI can enhance work across business and for customersExperience with the latest LLM's, RAG and Agentic AIExperience with vector DB'sLead MLOPs processesTrack developments in AI and translate relevant advances to implementEducationPhD or MSc in AI, DS, Engineering or other relevant STEMPackageTax free salaryVisaMedicalFlightsBonusRelocation allowances (cash, hotel, flights)If you feel you have the right skills, please apply and share your CV, so we can arrange a confidential discussion.**Due to the high volume of applicants, only relevant candidates will be contacted**

Lead Data Scientist – AI Lab | FintechLocation: Abu Dhabi My client is looking for a Lead Data Scientist to join a newly established AI Lab. This is a high-impact opportunity for a technically strong, hands-on Data Scientist who can take AI and machine learning solutions from concept through to production.The ideal candidate will have a strong Computer Science / technical academic background and be a genuine full-stack Data Scientist- someone who can write production-ready code, build models, work across the engineering stack and deploy solutions independently, rather than relying on separate teams to operationalise their work.Key ResponsibilitiesLead the design, development and deployment of advanced AI and machine learning solutions across banking use cases.Own the full lifecycle from problem definition, data preparation and modelling through to deployment, monitoring and optimisation.Develop production-grade solutions using Python, SQL and modern ML/AI frameworks.Build and productionise GenAI, LLM, RAG, NLP and agentic AI solutions where relevant.Work closely with Data Engineers, Software Engineers and Product teams to take AI solutions into production.Develop robust ML pipelines, APIs, model-serving components and MLOps processes.Establish best practices around model deployment, testing, monitoring, reproducibility and responsible AI.Evaluate emerging AI technologies and identify opportunities to create tangible value for the bank.Mentor Data Scientists and contribute to the technical direction of the new AI Lab.Ideal Candidate8+ years' experience in Data Science, Machine Learning, AI Engineering or a related technical field.Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Mathematics, Statistics, Engineering or another highly quantitative discipline.Strong hands-on Python development skills and excellent SQL.Proven track record of taking machine learning / AI models into production.Strong understanding of software engineering principles including Git, APIs, testing, CI/CD and containerisation.Experience with cloud platforms such as AWS, Azure or GCP.Experience with modern ML/AI technologies such as PyTorch, TensorFlow, Hugging Face, LLMs, RAG, vector databases and agentic AI is highly desirable.Banking, financial services or fintech experience would be advantageous.What Will Differentiate YouMy client is particularly interested in "full-stack" Data Scientists who are equally comfortable experimenting with models and engineering them into scalable production systems.Strong preference will be given to candidates with a rigorous Computer Science or technical background over candidates whose background is primarily business, commercial or MBA-focused.The successful candidate will be a builder -technically autonomous, hands-on and capable of taking an AI solution from an initial idea all the way through to production.*** Only successful candidates will be contacted ***

Detailed Job Description : Data ScientistROLE PROFILE • Designs and develops scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision-making processesKEY RESPONSIBILITIES• Work with large and complex data sets to solve challenging business problems• Collect, clean, and preprocess large datasets for analysis & model training• Perform exploratory data analysis (EDA) to uncover insights and inform model development• Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks• Build end-to-end ML pipelines , including data ingestion, transformation, model training, validation, and deployment• Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools• Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions PROFESSIONAL EXPERIENCE/QUALIFICATIONS • 5 years of experience in data science, machine learning, or AI• Expertise in supervised/unsupervised learning , deep learning, NLP, computer vision, or generative AI (e.g., LLMs).• Strong proficiency in Python and/or R ; familiarity with SQL for data querying• Ability to build data pipelines (Spark, Airflow, Hadoop) and work with big data tools• Understanding of model serving , API development (FastAPI, Flask), and optimizing model performance for real-time or batch inference.• Knowledge of Docker, Kubernetes, CI/CD pipelines , and tools like MLflow/Kubeflow for model lifecycle management (MLOps)• Experience deploying models on AWS, Google Cloud, Azure, or similar (e.g., Sagemaker, Vertex AI)• Educational qualifications: Master in Computer Science or a related field.

Senior Machine Learning (Senior to Staff level)About the opportunityWe are hiring for a rapidly growing advanced AI organisation based in Abu Dhabi, building and deploying sophisticated machine learning and generative AI systems for complex, high-impact real-world applications.The team operates at the intersection of applied AI research, machine learning engineering and production infrastructure, taking models from experimentation through to scalable, secure production environments.We are looking for a Senior Machine Learning who can bridge the gap between model development and production. Depending on your background, you may lean more heavily toward ML engineering, GenAI or ML infrastructure, but you should be comfortable owning significant parts of the end-to-end machine learning lifecycle.What you'll work onDesign, develop and productionise advanced machine learning and deep learning systemsBuild and deploy LLM and NLP pipelines, including fine-tuning, semantic search and Retrieval-Augmented Generation (RAG)Deploy and scale large language models using modern inference frameworks such as vLLM, Triton or TGIDevelop machine learning solutions across areas such as forecasting, classification, anomaly detection, risk modelling and optimisationBuild automated pipelines for training, fine-tuning, evaluation, versioning, deployment and retrainingContainerise and orchestrate ML workloads using Docker and KubernetesImplement reproducible ML workflows using platforms such as MLflow, Kubeflow or equivalent toolingDesign data processing pipelines covering both structured and unstructured dataImprove inference performance through techniques such as quantisation, distillation, pruning and distributed or multi-GPU inferenceMonitor production models for performance, drift, latency, throughput and resource utilisationBuild reliable CI/CD and infrastructure automation for machine learning systemsWork closely with researchers, data scientists, software engineers and domain experts to transform prototypes into robust production solutionsDevelop systems that operate within environments where security, reliability, explainability and engineering rigour are criticalWhat we're looking for5+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure or a closely related fieldStrong track record of deploying machine learning models into productionExpert-level proficiency in PythonStrong knowledge of modern ML/deep learning frameworks such as PyTorch, TensorFlow and Scikit-learnExperience with transformer-based architectures and modern NLP/GenAI ecosystems such as Hugging Face, Llama-family models, GPT-style models or equivalentHands-on experience with Docker, Kubernetes and production ML orchestrationExperience with MLflow, Kubeflow, SageMaker Pipelines or comparable MLOps platformsStrong understanding of model serving, inference optimisation, monitoring and lifecycle managementExperience building scalable data and model pipelinesStrong understanding of software engineering and algorithmic fundamentalsAbility to work across ambiguous technical problems and quickly adapt ML techniques to specialised domainsAny of the following would be advantageous:Production deployment of large language modelsRAG, semantic search, embeddings or document intelligenceFine-tuning and optimisation of transformer modelsTime-series forecasting, anomaly detection or predictive modellingAWS machine learning infrastructure, including SageMaker, EC2 or EKSOn-premise or highly secure ML deployment environmentsDistributed training or inference using tools such as DeepSpeed, FSDP or AccelerateInfrastructure as Code and ML-focused CI/CDC/C++ experience for performance-sensitive systemsWhy consider this role?This is an opportunity to work on technically challenging AI systems where models move beyond prototypes and are deployed against meaningful, real-world problems.You'll have access to strong engineering talent, modern AI infrastructure and substantial compute resources while working across LLMs, traditional machine learning and large-scale production AI systems.

MLOps EngineerLocation: Abu Dhabi, UAE Employment type: Full-timeInception42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.aiOverviewWe are looking for an MLOps Engineer to build and operate the systems that move machine learning from development into reliable production use. You will work across model training, validation, packaging, deployment, observability, and infrastructure automation, creating repeatable workflows that improve delivery speed without compromising security or control. The role works closely with data scientists, ML engineers, software engineers, architects, product teams, and platform teams in an environment where production quality, scale, and practical deployment matter.What You’ll OwnDesign, build, and operate end-to-end ML pipelines covering data preparation, training, validation, packaging, release, deployment, and production monitoring.Build CI/CD workflows for machine learning systems, with automated testing, quality gates, environment promotion, release controls, rollback, and reproducible builds.Own model lifecycle controls across experiment tracking, model and dataset versioning, registries, approvals, deployment governance, lineage, and production-readiness checks.Deploy and operate model-serving and inference workloads across production and non-production environments using containerised, cloud-native platforms.Create reusable platform components, templates, standards, and paved paths that reduce manual work and help teams move models from research to production consistently.Instrument ML systems for model quality, data and concept drift, latency, throughput, availability, infrastructure health, logs, traces, alerts, and service-level performance.Automate infrastructure and environment provisioning through infrastructure as code, configuration management, and disciplined controls for access, secrets, networking, storage, and compute.Debug and optimise pipelines, model-serving paths, and platform services at component and system level to improve reliability, scalability, performance, and cost efficiency.Embed security, compliance, access control, auditability, and data-governance requirements into ML platforms, automated workflows, and deployment processes.Collaborate closely with data science, engineering, product, and platform teams to resolve production issues, improve developer experience, document operating practices, and evolve the MLOps capability.What We’re Looking ForStrong engineering fundamentals across software delivery, machine learning systems, cloud infrastructure, automation, reliability, and production operations.A track record of building and operating production ML or data platforms, with clear ownership of deployment quality, reliability, security, and operational outcomes.Hands-on experience building CI/CD pipelines and automation using Python and shell scripting or comparable languages and tooling.Practical experience with containers and orchestration, including Docker and Kubernetes, and the ability to diagnose issues across applications, clusters, networks, storage, and compute.Working knowledge of Azure services for ML and application workloads, such as Azure Machine Learning, Azure AI Foundry, AKS, Azure DevOps, storage, networking, and managed data services, or equivalent cloud platforms.Experience with ML lifecycle and workflow capabilities such as experiment tracking, model registries, feature and dataset versioning, orchestration, and model serving using MLflow, Kubeflow, Airflow, DVC, or equivalent tools.Strong observability and debugging skills across model behaviour, data quality, drift, inference performance, distributed services, and infrastructure health.Clear communication and product judgement, with the ability to work through ambiguity, make pragmatic trade-offs, and collaborate effectively across research, engineering, platform, and product teams.Nice to HaveExperience deploying generative AI or LLM workloads, including prompt and model versioning, evaluation, guardrails, GPU environments, or high-performance inference.Exposure to distributed data platforms, search systems, SQL or NoSQL databases, time-series storage, feature stores, or large-scale training environments.Experience with Terraform or Bicep and observability platforms such as Prometheus, Grafana, ELK, Datadog, or Langfuse.

Role: Senior OR Principal AI EngineerSalary: DOELocation: Abu Dhabi HQ - 4-5 days per week on siteRole OverviewWe are looking for a Principal AI/ML Engineer to provide technical leadership across the design, development, deployment and continuous improvement of enterprise-grade AI and machine learning solutions.The Principal AI/ML Engineer will work closely with data scientists, software engineers, cloud engineers, architects, product teams and senior stakeholders to translate complex business problems into scalable, secure and measurable AI/ML solutions. The role will also play a key part in mentoring engineers, improving delivery practices, and embedding responsible AI and robust engineering principles across projects.Key Responsibilities:Lead the design, development and deployment of scalable AI/ML solutions, taking ownership of the full lifecycle from discovery and experimentation through to production delivery and continuous improvement.Define and govern AI/ML architecture, engineering standards and best practices, ensuring solutions are secure, maintainable, cost-effective and aligned with wider business and technology strategies.Design, build and optimise machine learning, deep learning and generative AI solutions, selecting the most appropriate tools, frameworks and approaches to solve complex business challenges.Drive the adoption of MLOps practices, including CI/CD, model versioning, monitoring, observability, performance optimisation and lifecycle management.Collaborate closely with business stakeholders, product teams, architects, data engineers and software engineers to translate requirements into practical AI-driven solutions that deliver measurable value.Provide technical leadership and mentorship to engineers, supporting capability development, design reviews, knowledge sharing and the adoption of engineering best practices.Lead technical discovery activities, proof-of-concepts and architecture assessments, helping shape AI strategy and identify opportunities where AI can improve business outcomes.Ensure robust documentation, governance and responsible AI principles are embedded throughout the delivery lifecycle, supporting transparency, compliance and operational excellence.Stay current with emerging AI technologies, frameworks and industry trends, evaluating their potential application and guiding the organisation's adoption of new capabilitiesRequired Experience:Extensive experience delivering AI/ML solutions in production environments.Strong Python expertise and experience with frameworks such as PyTorch, TensorFlow and Scikit-learn.Deep understanding of machine learning, deep learning and Generative AI technologies.Experience with Azure, AWS or GCP.Strong knowledge of MLOps, CI/CD, testing and software engineering best practices.Proven experience leading technical teams and influencing architecture decisions.Previous experience within a Consulting or Solutions Integrator environment

Exciting Opportunity Alert! 🌟 HTC Global Services is hiring AI Engineer for an 1-year extendable contract in Abu Dhabi, UAE (Onsite).HTC Global Services - a leading CMM level 5 global provider of innovative IT and Business Process Services and Solutions since 1990 with headquarters in Troy, Michigan, USA.About the Role:We are seeking an AI Engineer (Innovation) with 6+ years of experience with dtrong programming experience in Python. Expertise in Machine Learning, Deep Learning, NLP, and Generative AI. Hands-on experience with OpenAI, Azure OpenAI, LangChain, Semantic Kernel, AutoGen, or similar Agentic AI frameworks.Requirements:Design and develop AI/ML and Generative AI solutions.Build predictive models for demand forecasting, revenue optimization, dynamic pricing, ancillary revenue growth, and passenger behavior analysis.Develop and deploy LLM-based applications using RAG, Vector Databases, Prompt Engineering, and Agentic AI frameworks.Build intelligent recommendation and personalization engines.Develop scalable AI pipelines and integrate AI models with enterprise applications and operational systems.Deploy and manage AI solutions on Azure cloud platforms.Collaborate with Data Engineers, Data Scientists, Product Owners, and Business Stakeholders to translate business requirements into AI solutions.Ensure model performance, scalability, security, governance, and compliance standards.Required Skills:Experience building RAG architectures and Vector Databases.Strong knowledge of LLMs, Prompt Engineering, Fine-tuning, and AI Evaluation frameworks.Experience with Azure AI Services, Azure Databricks, Azure Machine Learning, Azure Data Factory, and Azure DevOps.Experience with MLOps, CI/CD, model deployment, monitoring, and governance.Knowledge of data engineering concepts, APIs, microservices, and event-driven architectures.Familiarity with SQL, Spark, and large-scale data processing.Interested candidates please do share your updated CV to mubeenakamal.basha@htcinc.com mentioning your Current CTC, expected CTC and notice period details.#HTCGlobalServices #ITJobs #hiring #lookingforjob #careers #jobs #immediatejoiner #recruitment #technology #jobseekers #interview #lookingforjobchange #newjob #AIEngineer #LLM #RAG #MCP #fastMCP #Python #LangGraph #LangChain #AzureAI

**RECRUITING**Contract OR PERM Open to relocators Job Title – Machine Learning EngineerSalary – N/AJob Type - Contract Or Perm Location – Abu DhabiStart Date – ASAPWe are working on a brand-new project, which is an Innovation Hub/Ai Lab backed by a large bank in the region.The candidate my client want is essentially a production-focused Machine Learning Engineer with 3–7 years' experience, rather than a pure Data Scientist. The major requirements I would source against are:3–7 years' experience building production-grade, scalable AI/ML systems.Very strong Python + ML engineering capability.Experience with supervised/unsupervised ML, deep learning, NLP, Computer Vision and/or GenAI/LLMs — you don't necessarily need every area but should have genuine depth.Building end-to-end ML pipelines: ingestion → transformation → training → validation → deployment.Production model serving and APIs using FastAPI / Flask.Strong ML system architecture understanding.Experience with real-time and/or batch inference and model performance optimisation.Knowledge of Docker, Kubernetes and CI/CD.Exposure to MLflow and/or Kubeflow for lifecycle management.Deployment onto AWS or Azure.Experience training/deploying traditional ML, neural networks and Agentic AI models.Ideally experience fine-tuning SLMs/LLMs and building more complex AI architectures.Thanks,Ollie

Senior Machine Learning EngineerLocation: Abu Dhabi, UAE Inception42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.aiOverviewWe are looking for a Senior Machine Learning Engineer to turn advanced machine learning work into reliable, scalable products. You will own the engineering path from data and experimentation through training, evaluation, deployment, and production monitoring, working closely with applied scientists, data scientists, MLOps engineers, software engineers, and product teams. The environment is technically demanding and deployment-focused: models must perform under real-world constraints for latency, reliability, security, governance, and cost.What You’ll OwnDesign, build, and scale production machine learning systems across data preparation, feature engineering, training, evaluation, deployment, and monitoring.Translate research prototypes into maintainable product capabilities, defining production-readiness criteria and closing gaps in data quality, performance, reliability, and operability.Build reusable training and evaluation pipelines with disciplined experiment tracking, model and dataset versioning, validation, benchmarking, and reproducibility.Engineer batch and online inference paths, model-serving components, APIs, and feature workflows that meet product requirements for latency, throughput, availability, and cost.Optimise models and systems across accuracy, robustness, memory, compute efficiency, inference speed, and scalability using evidence from profiling and real workloads.Instrument production ML systems for model quality, drift, data integrity, latency, throughput, availability, and operational health; diagnose issues across the full stack.Collaborate closely with MLOps, platform, and software engineering teams on CI/CD, containerisation, orchestration, release controls, rollback, and production operations.Embed security, privacy, governance, auditability, and compliance requirements into data, model, and deployment workflows from the start.Drive architecture and technical decisions for ML components, balancing product needs with maintainability, reliability, performance, and delivery speed.Raise engineering quality through design reviews, code reviews, technical documentation, reusable standards, and practical mentorship for other engineers.What We’re Looking ForStrong software engineering fundamentals and fluency in Python, with a track record of building tested, maintainable systems rather than isolated notebooks or prototypes.Experience shipping and operating production machine learning systems, including ownership of deployment quality, reliability, observability, and operational outcomes.Strong understanding of machine learning fundamentals and practical depth in one or more areas such as deep learning, natural language processing, computer vision, recommendation, or generative AI.Experience with modern ML frameworks such as PyTorch, TensorFlow, or scikit-learn, and sound judgment about selecting tools for the problem rather than applying a fixed stack.Working knowledge of data and training workflows, including SQL, data validation, feature engineering, distributed processing, and efficient use of large datasets.Practical experience with model serving, APIs, containers, orchestration, CI/CD, and cloud infrastructure in production environments.Strong debugging and systems-thinking skills across data, model behaviour, application code, distributed services, and infrastructure.Clear communication and product judgment, with the ability to work through ambiguity, make pragmatic trade-offs, and collaborate across research, engineering, platform, and product teams.Nice to HaveExperience with large language models, retrieval-augmented generation, agentic systems, evaluation frameworks, guardrails, or high-performance inference.Exposure to Azure Machine Learning, Azure AI Foundry, Databricks, AKS, or equivalent cloud-native AI and data platforms.Experience with distributed training, GPU optimisation, feature stores, vector search, or large-scale model-serving infrastructure.Contributions to reusable ML platforms, open-source systems, applied research, or technical standards that have improved engineering practice beyond a single project.

🔷 Join a Global Leader in Workforce Solutions – Net2Source Inc.🚀 Right Talent. Right Time. Right Place. Right Price.📍 Operating in 32 countries | 5,500+ Employees🌐 www.net2source.com | 📧 vinayak@net2source.ae | ☎️+971 589845977Job Title: Machine Learning EngineerJob Location: Abu Dhabi, UAEExperience: 4+ YearsEmployment Type: PermanentClient Domain: BankingRequired Skills & Competencies:• 3+ years of experience in building production grade, scalable AI systems.• Expert in supervised/unsupervised learning, deep learning, NLP, computervision, or generative AI (e.g., LLMs).• Strong ML system architecture skills• Understanding of model serving, API development (FastAPI, Flask), andoptimizing model performance for real-time or batch inference.• General Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools likeMLflow/Kubeflow for model lifecycle management (MLOps)• Comfortable with deploying models on AWS or Azure• Preferred Education qualifications: Master or PhD in Computer Science or arelated field.🔻 About Net2Source Inc.Net2Source offers global staffing solutions and enterprise-scale recruitment support.🏆 Recognized by INC 5000 & SIA🌍 Global offices: UAE | USA | India | UK | Singapore | Canada | ANZWe support inclusive hiring practices in accordance with UAE labor law.🔗 www.net2source.com/careers👥 Tagging my amazing colleagues for wider reach: Premalatha Selvam Smriti Singh Rintumol Chacko Remya Nair Hamsa B V Ankit Butola Suhas HUDDAR Reena Garg

Vatic is looking for an AI engineer in Abu Dhabi with proven experience with large foundational models. Our environment is highly collaborative, fostering innovation and growth. We are interested in a variety of data modalities (language, vision, multimodal, and structured data) and modern model architectures. We are looking for team players who are interested in developing next-generation platforms and tools for Machine Learning as well as conducting state-of-the-art research.As an AI Engineer you will:Play a crucial role in designing and developing cutting-edge frameworks that can serve as the future tools to help develop innovative platforms and systems that drive our trading and research efforts, with a different lens through foundational models. Work closely with a team of passionate researchers and engineers to make sure such frameworks are compatible with recent cloud platforms, popular ML frameworks and libraries, as well as modern model architectures.play a central role in defining and running large-scale experiments that contribute towards rigorous evaluation and in-depth model analysis.We employ a team of talented engineers who have been recognized as leaders in our field. We are passionate about hiring the best and brightest, empowering them with the tools and freedom they need to be successful.If you possess the following, we would love to explore what is available for you with our team:Keen interest in training, evaluation, understanding, and innovation of foundational modelsAdvanced proficiency with Python development in a Linux environmentStrong understanding of machine learning, deep learning, and generative AI methods and technologiesDirect experience with multi-GPU model trainingDemonstrable success building high performance softwarePerformance tuning/optimization of software and algorithmsFamiliarity with parallel computing models and frameworksStrong analytical and problem-solving skillsA passion for trading, research, and technologyExcitement and interest in using readily available open source technologies and prototypesExcellent communication skillsConfident team player motivated by a fast-paced environmentAt Vatic, we're serious about our work—but we also believe in balance, growth, and having fun along the way. Here's what you can expect:Flat structure with direct executive exposure – Work closely with leadership and make an impact from day one.Comprehensive health benefits – Full health insurance coverage for employees and dependents.Daily meals provided – Enjoy free lunch at the office.

AI/ML Engineer (Mid, Senior, Lead)Location | Abu Dhabi/Dubai (Hybrid)Comp | $200k-$250k + Equity + PackageFocus | Real-World AI Deployment Across Enterprise & Operational Environments, AI Systems, LLM Applications & Agentic WorkflowsWe’re working with a frontier AI company that builds and deploys AI systems for the world’s most important institutions, automating billion-dollar processes, unlocking national productivity, and modernising the infrastructure of society itself. This is not research for research’s sake. This is applied AI, deployed in the real world.They are backed by some of the world’s most respected founders and AI pioneers, and built a 70+ person team from tier-1 companies. Now, they’re expanding globally and looking for engineers who want to own the deployment of AI that changes how the world works.The RoleAs an AI/ML Engineer, you’ll turn state-of-the-art models into production-grade systems that power national-scale change. You’ll design, deploy, and optimise AI applications that automate some of the hardest real-world processes from energy and healthcare to government operations.You’ll be embedded in small, elite teams that own problems end-to-end: architecture, data, model design, deployment, and iteration. No silos. No bureaucracy. Just impact.What You’ll DoBuild and deploy applied AI systems: from LLMs and agentic reasoning models to scalable data pipelines and production infrastructure.Solve world-scale problems: automate complex workflows, improve public outcomes, and optimise critical national systems.Collaborate with leaders and clients: work directly with government officials, enterprise executives, and industry veterans to translate ideas into deployed solutions.Operate like a founder: own delivery, make decisions fast, and help shape the next generation of applied AI tooling.Stay ahead of the curve: explore the latest GenAI, LLM, and reasoning research to push models from theory to practice.You’ll Thrive Here If You…Have hands-on experience building GenAI or LLM-based applications (agents, RAG, reasoning models).Know your way around Python, PyTorch, JAX, or TensorFlow, with strong software engineering fundamentals.Can work across data pipelines, APIs, and production deployments with speed and precision.Enjoy autonomy, creative problem-solving, and owning solutions end-to-end.Want your work to matter to see it deployed across industries, governments, and communities.Why JoinBuild at frontier scale: ship real systems used by governments, hospitals, and energy leaders worldwide.Elite peers: collaborate with engineers and researchers from tier-1 places.Move fast, own outcomes: you’ll ship code in weeks, not quarters.Meaningful rewards: competitive comp, equity, and ownership tied to real impact.Support to thrive: daily lunches, full benefits, 401(k), and unlimited PTO.If you want to build AI that doesn’t just predict but powers economies, saves lives, and redefines what’s possible , this is where you do it.Join the team building AI that actually ships. Get in touch with danny@salientgroup.com.au for more information.

The Center for Interdisciplinary Data Science and Artificial Intelligence (CIDSAI) at NYU Abu Dhabi seeks to recruit a Research Engineer to support the design and implementation of interactive AI and data science platforms across the Center’s research clusters.The successful candidate will collaborate with researchers and faculty across domains such as Foundations of Data Science, Enabling Technologies, Health, Education and Personalized Learning, and Bias, Ethics & Privacy, contributing to the development of prototypes, interfaces, and software infrastructure that translate research outputs into usable tools.Key ResponsibilitiesDesign, develop, and maintain web-based and AI-powered platforms supporting CIDSAI research projects.Collaborate with faculty and postdocs on projects such as: Mathematical Collaboration Platform, Personalized Learning Systems and SmartClass AI Tutors, Health-focused conversational AI and multimodal interfaces, Bias detection and fairness-aware tools for ethical AI.Integrate machine learning models, visualization dashboards, and backend services.Contribute to data collection, testing, documentation, and dissemination of open-source resources.QualificationsBachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.Strong programming skills.Experience with machine learning frameworks and backend systems.Familiarity with cloud computing, API integration, data visualization, and database management.Ability to work in interdisciplinary research environments bridging theory, applications, and ethics.The appointment is for two years, with the possibility of renewal. The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates will be considered until the position is filled. To be considered, all applicants must submit a cover letter, curriculum vitae, transcript of degree, a maximum three pages summary of research accomplishments and interests, and at least 2 letters of recommendation, all in PDF format.Further questions may be directed to Professor Pierre Youssef at yp27@nyu.edu.About NYU Abu Dhabihttps://nyuad.nyu.edu/en/NYU Abu Dhabi is the first comprehensive liberal arts and research campus in the Middle East to be operated abroad by a major American research university. Times Higher Education ranks NYU among the top 30 universities in the world, making NYU Abu Dhabi the highest-ranked university in the UAE and MENA region. NYU Abu Dhabi has integrated a highly selective undergraduate curriculum across the disciplines with a world center for advanced research and scholarship. The university enables its students in the sciences, engineering, social sciences, humanities, and arts to succeed in an increasingly interdependent world and advance cooperation and progress on humanity’s shared challenges. NYU Abu Dhabi’s high-achieving students have come from over 120 countries and speak over 100 languages. Together, NYU's campuses in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents.NYUAD is committed to upholding a culture of non-discrimination, anti-harassment, dignity, and mutual respect; providing equal access and opportunity; and fostering academic excellence in learning, research, and teaching.UAE Nationals are encouraged to apply.Equal Employment Opportunity StatementFor people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdprNYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. All interested persons are encouraged to apply for vacant positions at all levels.Sustainability Statement NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more at nyu.edu/sustainability

As an AI Researcher at Vatic Labs in Abu Dhabi, you will research and develop innovative AI-driven quantitative trading strategies. You will explore vast amounts of market and alternative data, inventing and applying a new generation of state-of-the-art technologies that are inspired by large language models, deep neural networks, transformers, and advanced prompt engineering methods to discover and capitalize on trading opportunities.The nature of the problems we work on is challenging, hence we hire some of the world's top AI talent to develop novel AI methods and trading strategies. Our team of talented researchers and technologists has been recognized as leaders in their field. Our distinguished researchers have been widely cited for their publications in top-tier, peer-reviewed scientific journals. We are passionate about hiring the best and the brightest, empowering them with the tools and mentorship needed to be successful. Our environment is highly collaborative and open, sharing AI and trading expertise across team members and fostering innovation and growth.If you possess all or most of the following, we would love to explore what is available for you with our team:Earned or will earn a Master's or Ph.D. in a quantitative field, such as Computer Science, Electrical and Computer Engineering, Applied Math, Statistics, Quantitative Finance, Cognitive Science, Physics or another field of science.Experience analyzing large data sets with rigorous statistical and ML/AI approaches, including classification, clustering, regression (linear and nonlinear), optimization, signal processing, filtering and smoothing, time-series analysis, hidden Markov models, high-dimensional data analysis, vector quantization, decision tree methods, EM methods, Bayesian methods, variational inference methods, and neural networks.Demonstration of deep knowledge of large language models and deep neural networks for practical applications (e.g. NLP, vision, speech, signal processing, scientific computing, finance, etc). Finance applications preferred but not necessary.Ability to generate impactful research in academic or professional pursuits.Advanced understanding and experience of practical programming languages and software tools for data analysis and ML/AI applications, such as Python, C++, PyTorch, Tensorflow, etc.Interest and enthusiasm for learning about financial markets (previous experience not required).At Vatic, we're serious about our work—but we also believe in balance, growth, and having fun along the way. Here's what you can expect:Flat structure with direct executive exposure – Work closely with leadership and make an impact from day one.Comprehensive health benefits – Full health insurance coverage for employees and dependents.Daily meals provided – Enjoy free lunch at the office.

Not just a job, but a careerYokogawa, award winner for ‘Best Asset Monitoring Technology’ and ‘Best Digital Twin Technology’ at the HP Awards, is a leading provider of industrial automation, test and measurement, information systems and industrial services in several industries.Our aim is to shape a better future for our planet through supporting the energy transition, (bio)technology, artificial intelligence, industrial cybersecurity, etc. We are committed to the United Nations sustainable development goals by utilizing our ability to measure and connect.About The TeamOur 18,000 employees work in over 60 countries with one corporate mission, to "co-innovate tomorrow". We are looking for dynamic colleagues who share our passion for technology and care for our planet. In return, we offer you great career opportunities to grow yourself in a truly global culture where respect, value creation, collaboration, integrity, and gratitude are highly valued and exhibited in everything we do.We are looking for a Lead AI/ML Engineer with deep technical expertise and proven leadership in delivering impactful solutions for the oil & gas industry. In this role, you will drive the design, development, and implementation of advanced AI/ML models, working closely with cross-functional teams to optimize operations and deliver data-driven insights in challenging industrial environments.Job OverviewYou will be part of a Project Delivery team to:Conduct Site survey of the customer requirements and developDesign and develop AI models that support asset performance management across various maintenance strategies, including predictive, prescriptive, and cognitive maintenance.Implement Analytical AI techniques (supervised/unsupervised learning, reinforcement learning) for predictive maintenance, defect elimination, and asset strategy optimization.Leverage Generative AI (Large Language Models, Deep Reinforcement Learning) to enable multi-agent systems for collaborative decision-making and autonomous goal-seeking behavior.Use AI to analyze trends and patterns, make intelligent recommendations, and automate decision support systems.Develop intelligent recommendations systems that improve maintenance processes, criticality assessments, and reliability analytics.Ensure AI models are scalable and deployable within industrial platforms, integrating with PLC, DCS, SCADA, Historians, EAM, MES/MOM, SCM, and ERP systems.Ensure compliance with ethical AI principles, particularly in terms of fairness, transparency, and bias mitigation.Key ResponsibilitiesAI/ML Strategy & Solution DesignDefine the AI/ML strategy and roadmap for oil & gas solutions, aligning with Yokogawa’s digital transformation objectives.Architect end-to-end ML systems, from data ingestion and feature engineering to model deployment and monitoring.Technical Leadership & MentorshipLead and mentor a multidisciplinary team of data scientists, machine learning engineers, and software developers.Advocate best practices in model development, version control, MLOps, and engineering excellence.Data Pipeline & Model Lifecycle ManagementOversee data gathering, cleaning, and preprocessing from diverse sources (e.g., sensors, IoT devices, DCS, SCADA systems).Implement robust CI/CD pipelines to streamline model development, testing, deployment, and updates.Optimization & Real-time AnalyticsDevelop and optimize algorithms for real-time analytics and predictive maintenance in upstream, midstream, and downstream operations.Perform model performance tuning to ensure reliability and scalability under production environments.Stakeholder Collaboration & CommunicationPartner with domain experts, process engineers, and project managers to translate complex operational challenges into AI-driven solutions.Present technical outcomes to both technical and non-technical audiences, highlighting business value and ROI.Compliance & Risk ManagementEnsure all AI/ML solutions comply with industry regulations, safety standards, and data governance policies.Proactively address potential risks related to data privacy, model bias, and operational safety.RequirementsBachelor’s/master’s in computer science, AI, Machine Learning, or related field.8+ years of hands-on experience in AI/ML, with at least 3 years in a senior or lead role.Proven project delivery experience in industrial or energy sectors, with a preference for oil & gas.Strong experience with Predictive Analytics and Prescriptive Analytics using tools like TensorFlow, PyTorch, and Keras.Proven experience in Generative AI, RAG and vector embeddings for optimized knowledge retrieval and decision-making, and multi-agent systems for industrial applications.Strong foundation in machine learning algorithms (supervised, unsupervised, reinforcement learning), statistical modelling, and optimization techniques.Proficiency in handling large-scale data, time-series data, and sensor/IoT data within industrial contexts.Expertise in cloud-based AI deployments (AWS, Azure, or Google Cloud) and edge AI for real-time decision-making.Demonstrated knowledge of oil & gas processes (upstream, midstream, downstream), instrumentation, and control systems.Familiarity with real-time data challenges and solutions specific to high-stakes industrial environments.Experience with SCADA, DCS, PLCs, smart sensors, IoT platforms, and Historians.Strong analytical, problem-solving, and communication skills, with a proven ability to work across teams.Knowledge/ Professional Skills (Technical knowledge or skills required to perform the job)Programming & FrameworksLanguages: Proficiency in Python is essential; exposure to C++/Java or other languages is a plus.ML Libraries: Expert-level knowledge of TensorFlow, PyTorch, Scikit-learn; familiarity with Keras, XGBoost.MLOps & DevOpsExperience with CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and version control (Git).Exposure to logging, monitoring, and performance tuning tools to maintain high availability and performance of AI/ML solutions.Cloud & Big DataHands-on experience with major cloud platforms (AWS, Azure, or GCP) for model deployment and data processing.Familiarity with distributed data processing and big data technologies (Spark, Hadoop), plus knowledge of time-series databases (e.g., InfluxDB, OSIsoft PI) is an added advantage.Data Engineering & IntegrationUnderstanding of data ingestion workflows, ETL/ELT processes, and streaming data (Kafka, MQTT etc.).Experience integrating AI/ML solutions into existing industrial control systems and operational dashboards.Personal Attributes (Special Personal Characteristics/ Interpersonal Skills)Consistently demonstrates exceptional technical skills, competence, and productivity.Deeply passionate about transforming emerging technologies into practical, industrial solutions.Possesses excellent communication and interpersonal abilities.A fast learner with a strong aptitude for collaboration and teamwork.Yokogawa is an Equal Opportunity Employer. Yokogawa wants a diverse, equitable and inclusive culture. We will actively recruit, develop, and promote people from a variety of backgrounds who differ in terms of experience, knowledge, thinking styles, perspective, cultural background, and socioeconomic status. We will not discriminate based on race, skin color, age, sex, gender identity and expression, sexual orientation, religion, belief, political opinion, nationality, ethnicity, place of origin, disability, family relations or any other circumstances. Yokogawa values differences and enables everyone to belong, contribute, succeed, and demonstrate their full potential.Are you being referred to one of our roles? If so, ask your connection at Yokogawaabout our Employee Referral process!

Job Code: 6315Job Title: DevOps / MLOps Engineer (AI & LLM Platforms)Location: Abu DhabiContract: 1 year and renewableExperience: 7+ yearsRole PurposeThe DevOps / MLOps Engineer is responsible for the setup, automation, and maintenance of infrastructure and deployment pipelines for AI/ML and microservices-based applications. This role focuses on enabling efficient development, testing, and deployment of AI solutions, including LLM workloads, while ensuring system reliability, scalability, and performance.Key Responsibilities Infrastructure Support & Environment ManagementSet up and maintain compute infrastructure, including GPU-enabled environments.Configure and manage Linux-based systems for development and production environments.Provisioning and configuration of cloud and on-prem infrastructure.Monitor system resources and assist in performance tuning and optimization. Containerization & DeploymentBuild and manage containerized applications using Docker.Deploy and manage applications on Kubernetes clusters under guidance from senior engineers.Creating deployment configurations, Helm charts, and environment setups.Support scaling and orchestration of microservices and AI workloads. CI/CD Pipeline ImplementationDevelop and maintain CI/CD pipelines for application and AI model deployment.Automate build, test, and deployment processes using tools like Azure DevOps, GitHub Actions, or Jenkins.Ensure smooth promotion of code and models across environments (dev, test, prod).Troubleshoot pipeline failures and deployment issues. MLOps & AI Deployment SupportDeploying machine learning models and LLM-based services.Integration of AI components into production systems.Contribute to model versioning, monitoring, and lifecycle management.Work with AI engineers to operationalize RAG pipelines and inference services. Monitoring, Logging & Issue ResolutionImplement and maintain monitoring and logging solutions (e.g., Prometheus, Grafana, ELK).Track application performance, system health, and availability.Respond to incidents, troubleshoot issues, and escalate when required.Assist in root cause analysis and continuous improvement. Automation & ScriptingWrite scripts (Python, Bash) to automate repetitive operational tasks.Support Infrastructure as Code (IaC) initiatives using tools like Terraform or ARM templates.Improve operational efficiency through automation and tooling. Collaboration & SupportWork closely with Senior DevOps/MLOps Engineers, AI Engineers, and Development teams.Support developers in environment setup, debugging, and deployment processes.Follow DevOps and MLOps best practices and continuously improve operational workflows.Required Skills & QualificationsBachelor s degree in Computer Science, Engineering, or related field.7+ years of experience in DevOps or platform engineering roles.Basic to intermediate experience with Linux system administration.Hands-on experience with Docker and containerization.Familiarity with Kubernetes (deployment and basic management).Experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins, etc.).Basic understanding of cloud platforms (Azure, AWS, or GCP).Scripting skills in Python, Bash, or similar.Understanding of version control systems (Git).Preferred SkillsExposure to AI/ML model deployment and MLOps practices.Familiarity with LLM deployment concepts and tools.Basic knowledge of GPU environments and high-performance computing.Experience with monitoring and logging tools (Prometheus, Grafana, ELK).Knowledge of Infrastructure as Code (Terraform, ARM templates).Understanding of microservices architecture.Key Performance Indicators (KPIs)Deployment success rate and pipeline stability.System uptime and availability.Resolution time for incidents and issues.Efficiency of CI/CD processes.Infrastructure utilization and basic cost optimization.Support effectiveness for development and AI teams.Stakeholders & Reporting Reports to: Senior DevOps / MLOps Engineer / Platform LeadKey Stakeholders:AI Engineers & Data ScientistsBackend & Frontend DevelopersDevOps / Platform TeamQA & Release Management Teams

We are HCLTech, one of the fastest-growing large tech companies in the world and home to 219,000+ people across 54 countries, supercharging progress through industry-leading capabilities centered around Digital, Engineering and Cloud.The driving force behind that work, our people, are diverse, creative, and passionate, raising the bar for excellence on a regular basis. We, in turn, work hard to bring out the best in them as we strive to help them find their spark and become the best version of themselves that they can be.We are on the lookout for a highly talented and self-motivated AI/ML Operations Engineer to join us on our journey in advancing the technological world through innovation and creativity.ROLE PURPOSE: The AI/ML Operations Engineer is a role within the Centre of Excellence for Emerging Technologies, responsible for operationalizing, deploying, and supporting the bank’s Artificial Intelligence and Machine Learning solutions across enterprise platforms. Reporting to the Manager for Platform & Tools, this role is focused on the hands-on engineering, automation, monitoring, and support of AI/ML workloads – ensuring models and use cases run reliably, securely, and at scale in production. This role is focused on the day-to-day operations and engineering of AI/ML platforms and pipelines, with accountability for the stability, performance, and security of the AI workloads that support the bank’s high-priority AI initiatives. The following points highlight the primary responsibilities and contributions to this role: • Deploy, configure, and operate AI/ML solutions on Microsoft Azure AI Foundry and the bank’s approved AI platforms. • Build and maintain MLOps deployment and monitoring pipelines to run AI/ML models reliably in production. • Operate and administer the Citadel platform and other AI/ML tooling used across the bank, ensuring availability and correct configuration. • Develop automation scripts, utilities, and integrations in Python to streamline model deployment, data preparation, and operational tasks. • Build and maintain CI/CD pipelines for AI/ML workloads using DevOps toolchains. • Monitor AI/ML workloads for performance, model drift, reliability, and cost, and proactively resolve incidents. • Build and maintain Power BI dashboards and reports to track platform usage, model performance, and operational KPIs. • Support the onboarding and productionization of AI use cases, working with data scientists and business teams. • Implement and enforce security, access management, and governance controls across AI/ML environments. • Manage containerized and cloud-native deployments (e.g., Docker, Kubernetes) for AI/ML services on Azure. • Maintain technical documentation, runbooks, and standard operating procedures for AI/ML platforms and pipelines. • Coordinate with vendors and internal teams to manage upgrades, enhancements, patches, and production support. • Review and validate vendor-delivered code, configuration files, and pipelines, evaluating them against quality, security, and production-readiness standards before go-live. • Ensure AI/ML platforms adhere to internal IT governance frameworks and external regulatory requirements (e.g., UAE Central Bank). • Maintain access control, platform usage governance, and operational oversight aligned with the bank’s risk and security frameworks. • Support risk assessments, internal audits, and platform-level controls for responsible AI adoption.Specialist Skills / Technical Knowledge, Technical Competencies Required for this role:• Hands-on experience with Microsoft Azure AI Foundry for building, deploying, and operating AI/ML solutions.• Strong programming skills in Python for automation, data processing, and ML pipeline development.• Experience operating the Citadel platform and the AI technologies and tooling used at Customer.• Proficiency in Power BI for building dashboards, reports, and operational visualizations.• Working knowledge of MLOps practices, CI/CD pipelines, containerization (Docker/Kubernetes), and cloud native services on Azure.• Solid understanding of the AI/ML model lifecycle, monitoring, model governance, platform security, access management, and regulatory alignment.• Experience working in regulated environments, ideally within banking or financial services.Previous experience:3+ years of experience in AI/ML operations, MLOps, DevOps, or platform engineering roles, with hands-on expertise in deploying and operating AI/ML solutions in production.Practical experience with Microsoft Azure AI Foundry, Python, and Power BI is essential, along with familiarity with the AI platforms and tooling used at Customer, including Citadel.Banking or financial services experience preferred, with exposure to regulatory environments such as UAE Central Bank.Demonstrated ability to automate, monitor, and support AI/ML workloads, and to collaborate effectively with data scientists, engineers, and business teams. Knowledge of AI/ML model development concepts is desirable; however, building or modifying AI models is not part of this role.Why Us: -We are one of the fastest-growing large tech companies in the world, with offices in 50+ countries across the globe and 219,000 employees,Our company is extremely diverse with 165 nationalities represented.We offer the opportunity to work with colleagues across the globe.We offer a virtual-first work environment, promoting a good work-life integration and real flexibility.We offer comprehensive benefits for all employees.We are a certified great place to work and a top employer in 17 countries, offering a positive work environment that values employee recognition and respect.Equality & Opportunity for All: -Representing 165 nationalities across the globe, we pride ourselves on being an equal opportunity employer, committed to providing equal employment opportunities to all applicants and employees regardless of race, religion, sex, color, age, national origin, pregnancy, sexual orientation, physical disability or genetic information, military or veteran status, or any other protected classification, in accordance with federal, state, and/or local law.

OverviewAbout AnalogAnalog is pioneering the era of physical intelligence. Where digital intelligence transformed our online lives, Analog is transforming the lived world, making cities more adaptive, industries more resilient, services more human centered, and experiences more extraordinary.At the heart of our approach are living world models, continuously updated, enriched by every capture point, every device, every interaction. Within them resides Ana, the Analog Neural Agent. Ana perceives, adapts, and guides, transforming raw data into foresight and orchestration that improves outcomes across sectors, from safeguarding high-value assets to enhancing human performanceAs a Sr. Research Engineer, you will be responsible for building AI systems that can perform previously improbable tasks or achieve unprecedented levels of performance. We're looking for people with solid engineering skills (for example designing, implementing, and improving a massive-scale distributed machine learning system), writing bug-free machine learning code, and building the science behind the algorithms employed. The most outstanding deep learning results are increasingly attained at a massive scale, and these results require engineers who are comfortable working in large distributed systems.ResponsibilitiesPast experience in creating high-performance implementations of deep learning algorithmsHave experience working in large distributed systemsProvide technical perspective to develop ML best practices and influence engineering culture in the teamHave in depth knowledge of how the machine learning system interacts with systems around itEvaluate the tradeoffs of different ML models/systems and deploy best ML practicesExperience with Data/Feature Engineering, Training, Evaluation and Deployment of ML modelsQualificationsHave run at least small-scale ML experimentsLove figuring out how systems work and continuously come up with ideas for how to make them faster while minimizing complexity and maintenance burdenHave strong software engineering skills and are proficient in PythonComfortable working with researchers to enable them to develop the next generation of modelsWhat Working At Analog OffersCulture: A collaborative, globally minded team building the future of physical intelligence. We value curiosity, courage, and creativity, and we’re united by a belief that technology should amplify human potential rather than replace it.Impact: The opportunity to work on projects with national and global significance, from adaptive cities and resilient infrastructure to next generation sports, entertainment, healthcare, and energy. Every product you help build contributes to shaping a safer, smarter, more human centered world.Growth: Outstanding opportunities for learning and career development. You’ll collaborate with leaders across AI, robotics, mixed reality, and systems engineering, while working on industry first, frontier scale solutions.Rewards: A competitive compensation package with healthcare, education support, and generous leave benefits, reflecting the high value we place on our people and their families.If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible.

About Infinite PlInfinite pl is a digital-led tech firm driven to become a digital logistics pioneer by harnessing the power of people, data, and platforms. We orchestrate and build innovative platforms that tackle complex problems within logistics and adjacent sectors, enriching the experiences of governments, businesses, and residents through cutting-edge digital solutions.Role SummaryWe are looking for an AI Engineer who can turn priority use cases into working, evaluable agents. You will design context, tools, prompts, retrieval flows, agent loops, and evaluation bundles, and contribute to reusable agentic patterns that let us scale safely across Fast Track innovation and Production-grade delivery. This is a hands-on builder role for someone who pairs strong software engineering with practical LLM and agentic AI delivery.Key Responsibilities Design and build agentic AI solutions from concept to pilot or production, including agent role definition, autonomy boundaries, tool/data scopes, guardrails, and evaluation criteria Implement agent loops, tool-use patterns, context engineering, retrieval/RAG flows, HITL approval journeys, tracing, telemetry, and audit capture Select and justify models based on latency, cost, residency, reliability, and use-case fit; tune solutions for performance and operational cost Develop reusable Agent JD templates, prompt/context patterns, orchestration patterns, and evaluation assets that can be adopted across teams Partner with governance, platform, and integration teams to make agents gate-ready by design, including prompt-injection, PII leakage, and reliability controls Support complex and multi-agent solutions, including orchestration, memory, retrieval, reasoning, and agent-to-agent composition Move solutions through sandbox, staging, and production promotion paths, ensuring each agent is observable, versioned, tested, and auditable Mentor junior engineers and contribute to a strong engineering culture around quality, speed, learning, and responsible AI deliveryKey Requirements Strong software engineering background in Python and/or C#, with experience building production-grade APIs, services, or cloud-native applications Hands-on experience with LLMs, agent frameworks, and agentic design patterns such as Semantic Kernel, Microsoft Agent Framework / AutoGen, LangGraph, or comparable frameworks Practical experience with RAG, vector search, prompt/context engineering, system prompt design, tool calling, and model evaluation Ability to build and maintain evaluation sets, regression tests, and acceptance criteria for agent behaviour, reliability, and safety Experience integrating APIs, data sources, and tools into AI workflows; comfortable debugging across application, data, and model layers Understanding of cloud-native delivery, observability, CI/CD, secrets management, telemetry, and secure software development practices Strong communication skills with the ability to explain technical trade-offs to product, business, governance, and leadership stakeholdersPreferred Qualifications Azure AI Foundry, Azure OpenAI, Azure AI Search, Functions, Container Apps, Cosmos DB, Redis, OpenTelemetry, or equivalent cloud AI stack experience Experience with bilingual or Arabic/English AI products, evaluation design, and user-facing AI experiences Experience with multi-modal AI, including vision, voice, document intelligence, or video/avatar experimentation Prior work in government, regulated, sovereign cloud, or enterprise environments where auditability and data residency matter Technical leadership or player-coach experience, including mentoring engineers and raising engineering standardsTECH STACK / TOOLSAzure AI Foundry - Semantic Kernel - LangGraph - Azure OpenAI - AI Search - Cosmos DB - Redis - Functions, Container Apps - Eval harness - OpenTelemetryFIRST 90 DAYS SUCCESS At least one agent progresses through the full factory path into production or production-equivalent validation on a real source, with a passing evaluation bundle A reusable Agent JD, prompt/context, or orchestration pattern is contributed to the team library The AI engineering team improves first-time gate readiness through better patterns, testing, and documentationThis role is not. A pure model research role or a platform/landing-zone owner. This role builds working agents and the patterns that make them reliable.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

About AI71:AI71 is an industry leader in artificial intelligence, delivering innovative solutions that empower developers, businesses and governments to solve complex challenges. AI71 builds secure, enterprise-ready applications powered by cutting-edge technology—tailored for knowledge workers and sector-specific needs. AI71 bridges the gap between advanced AI and real-world impact. Guided by a strong commitment to research and responsibility, we create transformative solutions that drive progress and empower communities.The Role:As a Senior Machine Learning Ops Engineer at AI71, you’ll be responsible for developing, deploying, and optimizing machine learning models and systems that power our AI solutions. You will collaborate with cross-functional teams to translate business challenges into impactful AI products.What You'll Do:Model Development: Develop, train, and deploy predictive models that enhance the capabilities of our AI solutions.AI Technologies: Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts.Cross-Functional Collaboration: Partner with business stakeholders to understand objectives and translate them into actionable machine learning tasks.Model Optimization: Continuously monitor, evaluate, and improve models based on real-world performance and evolving business needs.Data Pipeline Maintenance: Implement and manage robust data preprocessing pipelines to ensure high-quality, reliable input data for model development.What You'll Bring:Master’s or Ph.D. in AI, Data Science, Computer Science, Statistics, or a related field.5+ years of experience in AI, Machine Learning, Reinforcement Learning (RL), Data Science, or a related field.Proven experience with Large Language Models (LLMs) and parameter-efficient fine-tuning methods (LoRA, QLoRA, etc.) techniques.Strong proficiency in programming languages like Python. Preference for candidates with a competitive coding background (e.g., ACM/ICPC, NOI/IOI, Top Coder, Kaggle).Experience in building and deploying LLM-powered applications using FastAPI/ Flask and Streamlit for front-end interfaces, solid understanding of REST principles, HTTP Methods, websockets and API lifecycle management;Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and libraries like Langchain, Langgraph, LlamaIndex.Experience with SQL, NoSQL (PostgreSQL, MySQL, MongoDB, Redis) and vector databases (Pinecone, Weaviate, pgvector) for structured data storage and semantic search.Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems.Excellent communication and collaboration skills with the ability to work across interdisciplinary teams.Strong communication, stakeholder management, and decision-making skills, with a passion for building diverse, inclusive engineering teams. Why AI71:Mission-Driven Work: Work on cutting-edge AI applications with a talented and passionate team, solving real-world challenges in critical sectors.Unparalleled Opportunity: This is a chance to innovate and solve real-world challenges using AI at a company with unique access to world-leading models and resources.Career Growth: We offer competitive compensation, benefits, and significant career growth opportunities as a foundational member of the team.World-Class Environment: Enjoy a flexible working environment and the latest tools & technologies needed to do your best work.

Role SummaryStellar Technologies is seeking a Machine Learning Engineer (GenAI) to design, build, and deploy next-generation AI systems combining Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks.In this role, you will bridge model development and production engineering — developing scalable AI pipelines, integrating real-time APIs, and ensuring high-performance AI services that power enterprise-grade solutions. You will work at the intersection of machine learning, cloud infrastructure, and applied research, collaborating with top engineers and data scientists to deliver intelligent, production-ready AI capabilities.Key ResponsibilitiesDevelop and optimize AI systems leveraging LLMs, RAG, and agentic AI frameworks (LangChain, LangGraph).Build and deploy production-grade ML pipelines with real-time inference and retrieval components.Design and manage APIs and streaming services to integrate AI models into enterprise platforms.Implement containerized, orchestrated deployments using Docker, Kubernetes, and Azure ML.Automate data preprocessing, model training, evaluation, and versioning pipelines.Collaborate with cross-functional teams to integrate models into front-end, analytics, and automation workflows.Ensure governance, compliance, and security of deployed AI workloads.Conduct performance benchmarking and optimize inference latency and cost.Monitor AI systems in production using observability frameworks (logging, metrics, tracing).Participate in architecture discussions to enhance scalability and reliability of AI services.Required Skills & ExperienceStrong hands-on experience with LLMs, RAG, and agentic frameworks (LangChain, LangGraph, Semantic Kernel, etc.).Proficiency in Python, with deep understanding of ML libraries like PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers.Solid experience in API and microservices engineering (FastAPI, Flask).Familiarity with streaming architectures and real-time data handling.Knowledge of cloud platforms (Azure preferred), including Azure AI, Cognitive Services, and ML Ops.Experience with containerization and orchestration (Docker, Kubernetes).Understanding of vector databases (Pinecone, Weaviate, FAISS) and retrieval mechanisms.Experience in CI/CD, model deployment, and production monitoring.Preferred SkillsExposure to GPU-based inference optimization and serverless deployment.Knowledge of observability and monitoring tools for AI (Prometheus, Grafana, Azure Monitor).Experience in model fine-tuning, prompt engineering, or agentic orchestration.Understanding of AI governance, ethical AI, and data privacy frameworks.Soft SkillsStrong analytical and problem-solving mindset.Excellent collaboration and communication skills.Passion for innovation, experimentation, and applied AI.Job Category: Software EngineerJob Type: Full TimeJob Location: Abu Dhabi

Our MissionRebuild how the world works, to make institutions work better for the people they serve.About Brain Co.Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.Why NowBrain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now.Machine Learning Engineer, Applied AIAbout The RoleSo much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems.BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before.As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors.This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade — and the feedback loops are real, because our systems move real workflows forward every day.Who We're Looking ForYou understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered — never as magic.Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once.The Problems You'll Work OnComposite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML.Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them.Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them.Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off.Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops.In This Role, You WillTurn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments.Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production.Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on.Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done.Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark.Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

SummaryWe are seeking experienced and innovative AI Scientists to join our Asset Management team. This role focuses on developing AI-powered applications that enhance operations, optimize maintenance strategies, extend asset life, reduce operating costs, and improve equipment safety. The ideal candidate will be passionate about applying AI technologies—including LLMs (Large Language Models) and SLMs (Small Language Models)—to real-world industrial challenges.Principle Responsibilities and DutiesDesign, develop, and deploy AI applications to support:Predictive and preventive maintenanceAsset life extension strategiesOperational cost reductionEquipment safety and reliabilityLeverage LLMs/SLMs for intelligent automation and decision supportCollaborate with cross-functional teams to identify AI opportunitiesWork within Azure cloud environments and integrate with Microsoft Copilot toolsEnsure all work complies with company policies, procedures, and confidentiality standardsProvide structured, timely updates and reports on project progressExplore and evaluate emerging AI technologies and methodologiesDemonstrate flexibility in adapting to evolving project needs and prioritiesQualificationsBachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or a related fieldMinimum of 3 years of experience in AI/ML development, preferably in industrial or asset-intensive environmentsProficiency in programming languages such as Python, R, or similarExperience working with Azure cloud services and AI toolsFamiliarity with Microsoft Copilot and its integration into enterprise workflowsStrong communication skills in English (written and verbal)Ability to work independently while maintaining alignment with team goalsWillingness to travel internationally as requiredAdherence to strict confidentiality and data protection standardsStructured reporting and documentation practicesCollaborative and proactive mindsetCommitment to continuous learning and innovationExperience with asset management systems or industrial operations in gas compression and gas processing industryKnowledge of maintenance management frameworks (e.g., RCM, CBM)Understanding of data governance, security, and compliance in AI applicationsAbility to translate business needs into technical solutionsPhysical RequirementsAbout Us: Transforming Energy for a Sustainable Future. Enerflex is a premier integrated global provider of energy infrastructure solutions, covering gas processing, compression, power generation, refrigeration, cryogenic, energy transition, and water solutions. We proudly employ more than 5,000 highly skilled employees across over 25 countries worldwide.Enerflex places its core Values of Integrity, Commitment, Creativity, and Success into action across all aspects of the business. Our innovation, strong commitment to safety, and superior service to our customers positions us as the optimal partner to meet the needs of the dynamic energy industry.We foster a culture of ensuring our employees have the tools to do their jobs right, but to also thrive in their roles. We pride ourselves in our competitive wages, extensive rewards programs, celebration of successes of individuals and teams, and are committed to ensuring Enerflex remains a safe and inclusive space for all.Enerflex is an Equal Opportunity EmployerEnerflex grants equal employment opportunity to all qualified persons without regard to race, color, sex, age, national origin, religion, genetic information, mental or physical disability, or any other characteristic protected by applicable federal, state, or local law. Equal employment opportunities are provided in recruitment, hiring, promotions, wages, benefits, and other terms, conditions, and privileges of employment.DisclaimerThe above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, tasks, duties, knowledge, skills, and abilities required.

About AI71:AI71 is an industry leader in artificial intelligence, delivering innovative solutions that empower developers, businesses and governments to solve complex challenges. AI71 builds secure, enterprise-ready applications powered by cutting-edge technology—tailored for knowledge workers and sector-specific needs. AI71 bridges the gap between advanced AI and real-world impact. Guided by a strong commitment to research and responsibility, we create transformative solutions that drive progress and empower communities.The Role:As a Senior Machine Learning Engineer at AI71, you’ll be responsible for developing, deploying, and optimizing machine learning models and systems that power our AI solutions. You will collaborate with cross-functional teams to translate business challenges into impactful AI products.What You'll Do:Model Development: Develop, train, and deploy predictive models that enhance the capabilities of our AI solutions.AI Technologies: Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts.Cross-Functional Collaboration: Partner with business stakeholders to understand objectives and translate them into actionable machine learning tasks.Model Optimization: Continuously monitor, evaluate, and improve models based on real-world performance and evolving business needs.Data Pipeline Maintenance: Implement and manage robust data preprocessing pipelines to ensure high-quality, reliable input data for model development.What You'll Bring:Master’s or Ph.D. in AI, Data Science, Computer Science, Statistics, or a related field.5+ years of experience in AI, Machine Learning, Reinforcement Learning (RL), Data Science, or a related field.Proven experience with Large Language Models (LLMs) and parameter-efficient fine-tuning methods (LoRA, QLoRA, etc.) techniques.Strong proficiency in programming languages like Python. Preference for candidates with a competitive coding background (e.g., ACM/ICPC, NOI/IOI, Top Coder, Kaggle).Experience in building and deploying LLM-powered applications using FastAPI/ Flask and Streamlit for front-end interfaces, solid understanding of REST principles, HTTP Methods, websockets and API lifecycle management;Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and libraries like Langchain, Langgraph, LlamaIndex.Experience with SQL, NoSQL (PostgreSQL, MySQL, MongoDB, Redis) and vector databases (Pinecone, Weaviate, pgvector) for structured data storage and semantic search.Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems.Excellent communication and collaboration skills with the ability to work across interdisciplinary teams.Strong communication, stakeholder management, and decision-making skills, with a passion for building diverse, inclusive engineering teams. Why AI71:Mission-Driven Work: Work on cutting-edge AI applications with a talented and passionate team, solving real-world challenges in critical sectors.Unparalleled Opportunity: This is a chance to innovate and solve real-world challenges using AI at a company with unique access to world-leading models and resources.Career Growth: We offer competitive compensation, benefits, and significant career growth opportunities as a foundational member of the team.World-Class Environment: Enjoy a flexible working environment and the latest tools & technologies needed to do your best work.

Job Title: AI Engineer (ML, LLM, RAG & Agentic AI)Job Code : JPC 6310Experience: 2+ years of experience in AI/ML development.Education: Bachelor s degree in Computer Science, AI, Data Science, or related field.Language Skills: EnglishRole PurposeThe AI Engineer is responsible for developing, testing, and deploying AI/ML andGenerative AI solutions, including LLM-based applications, RAG pipelines, andAgentic workflows. This role also includes the ability to deploy and run LLMs onlocal/on-premise servers, supporting secure and efficient AI solution delivery inenterprise environments.Required Skills & QualificationsBachelor s degree in Computer Science, AI, Data Science, or related field.2+ years of experience in AI/ML development.Strong programming skills in Python.Hands-on Experience WithMachine learning frameworks (Scikit-learn, PyTorch, or TensorFlow)Basic NLP conceptsUnderstanding of ML model lifecycle (training, evaluation, deployment).Familiarity with REST APIs and integration concepts.Experience with Git/version control.