AI Jobs in Abu Dhabi

AI Engineer / Forward Deployed Engineer - AI & Agentic SystemsAbout the RoleDomain Rewiring means redesigning the operational domain itself — workflows, decision points, data flows and human roles — so that agentic AI becomes embedded into how work actually gets done.The Forward Deployed Engineering team works directly inside government entities to identify, build and deploy these systems, ensuring the underlying AI platform is shaped by real operational requirements rather than developed in isolation.Why JoinWork on complex applied AI problems. Re-engineer live government workflows and deploy agentic AI where operational decisions are actually made.Own outcomes, not just engineering. Define the users, workflow, success metrics and scope, then take the solution through to production and adoption.Small, senior teams. Work in compact squads of experienced engineers with significant autonomy and direct access to senior government stakeholders.Build the practice. Help establish the playbook for evaluation, AI-native software development and forward deployment, before helping grow the team around it.UAE-based opportunity. Relocation assistance is available for successful candidates and their families.The RoleWe are hiring a AI Engineer / Forward Deployed Engineer to join a small team of senior engineers responsible for taking complex government domains from discovery through to production AI systems.This is an individual contributor role first.You will personally own some of the organisation’s most challenging domain rewirings, working directly with users and senior stakeholders to understand how an operation works today and redesign how it should operate in an AI-native environment.There is no traditional business analyst, product manager or architect sitting between you and the problem. You define the users, workflow, scope, success metric and value case — and then build.You will own domain rewiring, adoption and realised business value, while partnering with specialists across AI engineering, product engineering, platform architecture and evaluation.As the programme grows, you may also lead a rewiring pod, hire engineers or own part of the domain portfolio. However, leadership sits alongside hands-on delivery rather than replacing it.What You OwnEmbed and define. Work directly inside government entities, understand how operations actually run and define the requirements yourself. Establish the metric, capture the baseline and agree it with the business owner before building.Design and ship. Architect agentic workflows spanning orchestration, retrieval, tools, agents, evaluation and human-in-the-loop processes — then write the production code.Navigate enterprise constraints. Take deployments through security, privacy, tenant isolation, governance and information-security reviews, creating reusable patterns for future deployments.Own adoption. Redesign the human process alongside the software. Success means changing how decisions are made, reducing cycle times, lowering costs or improving measurable outcomes.Prove value. Instrument products so their impact can be measured against an agreed baseline. Recommend scaling, redesigning or stopping initiatives based on evidence.Make the next deployment faster. Convert lessons from individual rewirings into reusable platform capabilities, engineering patterns and playbooks.First Three MonthsIndicative outcomes include:Delivering one lighthouse domain rewiring end-to-end within a major government entity, reaching production and demonstrating measurable improvement.Establishing an agreed value case and baseline with the relevant business owner and delivering the first value report.Contributing reusable capabilities to the underlying sovereign AI platform based on requirements discovered through real-world deployment.Who You AreStrong production engineer.You have personally shipped software end-to-end. Years of experience matter less than what you have successfully put into production.Field-hardened.You thrive in ambiguous, embedded and stakeholder-facing environments where the problem has not yet been clearly defined.Hands-on with agentic AI.You have practical depth across LLM and agent systems, including orchestration, retrieval, evaluation, agent design and prompt engineering. AI coding tools are part of your normal development workflow.Product owner by instinct.You are comfortable interviewing users, defining requirements, prioritising problems and cutting scope without waiting for a Product Manager.Business literate.You can discuss cost, cycle time, productivity, headcount and ROI with senior executives without hiding behind technical terminology.Enterprise literate.You understand identity, networking, data governance, security and approval processes required to move software into production within complex organisations.Value disciplined.You are willing to stop, redesign or descope your own work when the evidence shows it will not create sufficient value.Strongly PreferredForward Deployed Engineering, Solutions Engineering or technical delivery experience.Experience building or leading an FDE capability.Delivery experience within government, financial services or another regulated/high-assurance environment.Founder, founding engineer or zero-to-one product experience.Hands-on cloud and platform engineering experience, particularly Azure.Arabic and English capability, or experience operating effectively within bilingual environments.Evidence We Will Ask ForWe will want to understand:Two products you personally built that reached real users in production, including your contribution, scale and measurable outcome.For at least one product, the baseline before deployment, measured result afterwards, how the impact was measured and who validated it.One product you stopped, redesigned or significantly descoped because the expected value did not materialise, including the evidence behind that decision.Projected benefits in a business case are not considered realised impact.How Performance Is MeasuredPerformance is based on three primary outcomes:Speed: Time from problem definition to a rewired workflow being used in production.Value: Measurable impact against an agreed baseline, validated by the relevant business owner.Judgement: Rewirings that successfully pass the value checkpoint — and initiatives correctly stopped early when the evidence does not support further investment.Performance is not measured by lines of code, number of pilots or size of team.What This Role Is NotNot a management role disguised as engineering.You may lead engineers or a portfolio, but you remain personally responsible for delivering rewirings.Not advisory.Recommendations that end with a presentation or strategy document do not count. You build and ship.Not a pilot factory.A portfolio of proofs of concept that never reaches production or creates measurable value is not considered successful.

About the RoleGlobal Aerospace Logistics (GAL) is seeking a talented and driven AI Machine Learning Engineer to join our AI and Digital Transformation team. This role is responsible for designing, developing, deploying, and maintaining machine learning solutions that drive operational efficiency, automation, and data-driven decision making across the organization.You will work closely with data scientists, software engineers and business stakeholders to build scalable AI applications and bring machine learning models into production environments.Responsibilities:Design, build, train, and deploy machine learning models.Develop scalable data pipelines and feature engineering workflows.Productionize AI and machine learning solutions.Deploy models through APIs, microservices, and cloud platforms.Monitor, retrain, and optimize model performance.Implement MLOps practices including CI/CD, model versioning, and monitoring.Ensure data quality, security, and compliance requirements are met.Document AI systems, models, and technical workflows.Collaborate across multidisciplinary teams to deliver AI initiatives.Qualifications:Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.Strong programming skills in Python.Experience with Java, C++, or JavaScript is an advantage.Experience Required:3-8 years of experience in Machine Learning, Data Science, or Software Engineering.Minimum 2 years of hands-on experience deploying machine learning models into production.Experience in machine learning model development, feature engineering, data pipelines, and model deployment, with knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow.Experience with Generative AI, LLMs, NLP, Computer Vision, or similar AI technologies is an advantage

AI Engineering Manager Abu Dhabi | Government / Public Sector Our client, a major Abu Dhabi government entity, is searching for an AI Engineering Manager to help lead the design, build, and deployment of production-grade AI platforms and solutions.This is a hands-on technical leadership role for someone who combines strong AI engineering depth with the ability to lead engineers, shape technical roadmaps, and deliver secure, scalable AI systems in a complex enterprise environment.This role would suit someone who has built real AI systems, led technical teams or squads, and is now ready to take ownership of AI engineering delivery within a major government transformation programme.Candidate requirements:Strong background in AI engineering, machine learning engineering, software engineering, computer science, electrical engineering, or a related technical discipline.Proven experience building and deploying production AI / ML / GenAI systems, not just PoCs or strategy documents.Ability to lead or mentor engineers, manage technical delivery, set engineering standards, and review implementation quality.Experience working in enterprise, government, banking, defence, sovereign AI, or other regulated environments would be highly relevant.Strong understanding of responsible AI, privacy, security, governance, auditability, and AI risk controls.Comfortable engaging with senior stakeholders while remaining technically credible with engineering teams.Hands on experience with Python, APIs, backend engineering, cloud-native systems; LLMs, RAG, embeddings, prompt/context engineering; MLOps / LLMOps, model deployment, monitoring, evaluation, guardrails; Docker, Kubernetes,Qualified candidates should apply to register their interest.

ABOUT THE ROLEIn this role, you will design and build next-generation applications powered by AI agents on the Microsoft Azure ecosystem. You'll own features end-to-end, from responsive front-end experiences to scalable back-end services and agent orchestration layers, building applications for, driven by, and consumed by AI agents - combining strong full stack fundamentals with cutting-edge agentic AI development.Please note that this is a hybrid role, requiring office attendance at least twice a week.RESPONSIBILITIESDesign and build full stack, production-grade applications based on AI agents and multi-agent architectures.Develop modern, responsive web UIs supporting conversational experiences, real-time streaming agent responses, and dashboards for monitoring agents.Build robust back-end services and APIs (REST, gRPC, MCP) in Python (FastAPI), enabling agent-to-agent and agent-to-system communication.Develop agentic workflows using frameworks such as Semantic Kernel, AutoGen, LangChain/LangGraph, or Azure AI Agent Service.Integrate LLMs via Azure OpenAI Service, including prompt engineering, function/tool calling, and RAG pipelines.Design chunking and indexing strategies for RAG pipelines (semantic chunking, hybrid search, re-ranking) to optimize retrieval quality.Build and maintain evaluation pipelines for LLM/agent outputs (e.g., RAGAS, promptfoo, or custom eval frameworks) to measure accuracy, relevance, and reliability.Design cloud-native solutions using Azure Functions, AKS, Container Apps, Cosmos DB, and Azure AI Search, including data models across SQL and NoSQL stores.Implement secure, observable, cost-optimized systems using Azure Monitor, Key Vault, Entra ID, and CI/CD pipelines with infrastructure-as-code.REQUIREMENTS8+ years of full stack engineering experience, with at least 3 years on the Azure stack.Strong back-end skills in Python, with a solid grasp of distributed systems and microservices.Hands-on experience building applications with LLMs and agent frameworks (Semantic Kernel, AutoGen, LangGraph, or similar).Strong front-end proficiency in modern frameworks, typed JavaScript, semantic HTML/CSS, and responsive, accessible UI design.Experience with Azure OpenAI, Azure AI Foundry, and vector databases (Azure AI Search, Cosmos DB vector search).Proven experience with RAG architectures, embeddings, chunking/indexing strategies, prompt orchestration, and tool/function calling.Hands-on experience with evaluation frameworks for LLM/agent outputs and defining quality metrics for AI systems.Experience designing end-to-end agentic solution architectures (multi-agent orchestration, tool/agent boundaries, error-handling and fallback strategies).Experience building real-time features (WebSockets, SignalR, SSE) for streaming AI/agent responses.Solid understanding of authentication/authorization (OAuth2, Entra ID) and DevOps practices (CI/CD, IaC, Docker/Kubernetes).SoftServe is an equal opportunity employer. Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.

Company OverviewOpen Innovation AI is a global technology company that specializes in developing advanced solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The platform is hardware-agnostic, optimized for various GPUs and accelerators hardware, and facilitates seamless integration and scalability for enterprise AI applications. Open Innovation AI focuses on optimizing and simplifying AI workload management and making AI technologies accessible to organizations of all sizes. With its innovative solutions, companies can reduce operational costs, accelerate time to value, and maximize their return on investment, ensuring that their AI strategies contribute directly to enhanced business outcomesRole Overview:You will work directly with customers on their hardest problems, then design and build AI systems to solve them.Most of what you build will sit on top of the OI Platform, which provides the underlying infrastructure: model inference, agent orchestration, retrieval and the broader AI tooling layer. Your focus is not on rebuilding the foundation, but on applying it effectively. You will concentrate on the customer-specific layer: workflows, data integrations, user interfaces, and the points where the platform meets a live operation.Teams are small, autonomy is high, and you own each project from kickoff through production. A typical day may involve discussing architecture with a colleague, preparing customer data, building a web application on top of the platform, integrating with SAP or Oracle, prototyping an agent, and presenting results to executives.**You build it, you ship it, you own it**This is a strong environment for engineers who want to develop real depth. You will learn how to break down ambiguous problems, evaluate real tradeoffs, work directly with modern AI tooling, and grow within a team that will challenge you.Role Responsibilities:Embed with the client and work alongside their stakeholders to translate business problems into technical solutions.Build AI solutions on top of the OI Platform, including agent workflows, RAG pipelines, custom applications, and data products that connect the platform to the customer's enterprise systems.Own the full lifecycle: discovery, architecture, build, deployment, and iteration once real users are on the system.Transform large, complex, real-world data into formats that AI systems can use effectively.Collaborate with product engineers, deployment strategists, and the customer's team on both the solution and the surrounding strategy.Travel to client sites when the work calls for it: running workshops, debugging live systems, and building trust with the people you are delivering forRequired skills & Experience:A strong engineering background in computer science, software engineering, mathematics, physics, or data science.4+ years in backend, data, or full-stack engineering. More senior and more junior candidates are welcome to apply; please indicate where you sit.Genuine proficiency in Python or TypeScript (either is acceptable).Hands-on experience with modern AI tooling: LLMs, RAG, vector databases, agent frameworks, evaluations, and prompt orchestration.Experience working with large-scale data, cloud platforms (AWS, GCP, or Azure), and modern data tooling.The ability to hold a substantive conversation with a CTO and a non-technical executive in the same meeting.A strong sense of ownership: you make decisions, ship work, and move forward without waiting to be directed.Comfort with ambiguity, shifting priorities, and problems that have not been solved beforePreferred Experience:Experience deploying AI/ML systems in regulated or mission-critical environments.Experience working directly with enterprise customers in a delivery or consulting capacity

AI ResearcherAbu Dhabi, UAE | Full-time, on-siteA well-capitalised quantitative investment firm in Abu Dhabi is building out its AI research function and hiring AI Researchers to work on problems at the intersection of modern machine learning and financial markets.The team is small, highly technical and research-led. You'll have the freedom to pursue original ideas, access to serious compute, and a direct line from research to production, where models inform real investment decisions.What you'll work onPretraining and post-training large models, including reinforcement learning based approachesFoundation models for financial time series, built and trained from the ground upMulti-agent LLM systems that support and automate investment researchSystematic alpha research using modern deep learning methodsTaking research from idea to production, working closely with engineering and investment teamsWhat we're looking forA Master's or PhD in a quantitative field such as computer science, mathematics, statistics or physicsA strong track record in published research, building models at scale in industry, or strategies running in live productionHands-on experience with modern deep learning, including Transformers, reinforcement learning and sequence modellingProficiency in deep learning frameworks such as PyTorch or TensorFlowStrong programming skills in Python, with C++ a plusExperience at a leading AI research lab, big tech research team, or quantitative trading firm or hedge fund is highly valuedStrong research instincts: you can frame an open-ended problem, design rigorous experiments and communicate results clearlyQuant research experience is highly desirable, particularly applying deep learning to signal generation or systematic strategies Prior finance experience is welcome but not requiredWhy joinDirect impact on firm P&L, with your research driving live investment decisionsA collaborative environment where researchers, engineers and investment professionals work side by sideAccess to advanced technology and large-scale computeGenuine research ownership in a team that is still being shapedWork that goes into production rather than staying on paperHighly competitive, tax-free compensation with a performance-based bonusFull relocation and visa support to Abu DhabiInterested? Apply now for a confidential conversation.

🚀 AI Forward Deployed Engineer | Generative AI | Abu Dhabi 🇦🇪💰 AED 40,000 - 50,000 per month TAX FREE + Outstanding Benefits + Hybrid Working + £10,000 Relocation Package if outside UAEMUST 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 solutionsScoping projects during pre-salesWork directly with strategic enterprise customersBuild AI applications, LLM workflows and Agentic AI systemsPrototype 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✔ Background in Software Engineering & Applied AI development ✔ 3+ years scoping projects during pre-sales✔ Customer-facing consulting experience & come from client-facing role✔ Excellent communication and stakeholder management skills✔ Comfortable translating business challenges into technical AI solutions✔ Explaining technical concepts to non-technical stakeholdersTechnical background:✔ Experience deploying AI/ML solutions into production✔ Python or JavaScript development✔ Generative AI, LLMs, GPT, Agentic AI or AI applications✔ Cloud experience (Azure or AWS)Why 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

We're looking for a Lead Data Scientist to join our team in Abu Dhabi, UAE in an onsite working mode. In this role, you will lead advanced computer-vision work for video-analytics platforms. You will drive architecture decisions, guide model development and oversee production deployment. This role offers the opportunity to deliver impactful analytics solutions using machine learning in a dynamic digital environment.ResponsibilitiesLead computer-vision solutions for object detection, tracking, re-identification, action recognition and segmentation Drive model selection and experiments with YOLO, Transformer, ViT and RF-DETR Guide the use and fine-tuning of Vision-Language Models for CV tasks Optimize models for production using TensorRT, ONNX and OpenVINO Architect multi-camera tracking and video-analytics systems Manage the MLOps lifecycle: training, deployment and monitoring Mentor and review the code of data scientists and engineers Present technical direction and solution architecture to stakeholders RequirementsBachelor’s or Master’s degree in Computer Science, Engineering or related field (PhD a plus) 10+ years of applied machine-learning experience with deep computer-vision expertise Strong Python skills and hands-on experience with PyTorch and TensorFlow Proven use of YOLO, Transformer, ViT and RF-DETR architectures Experience with Vision-Language Models and fine-tuning for CV tasks Skills in model optimization: TensorRT, ONNX, OpenVINO and edge-AI constraints Track record of building multi-camera tracking and video-analytics systems Knowledge of MLOps lifecycle including training, deployment and monitoring Experience with team leadership, code review and mentoring Stakeholder communication skills Nice to haveExperience with Large Language Models (LLM) and advanced TensorFlow techniques Exposure to solution architecture, MLOps frameworks Proficiency with model optimization tools such as OpenVINO, ONNX Runtime and TensorRT Hands-on practice with object detection, PyTorch and Python developmentWe offerEnd of service gratuityPrivate healthcare and life insuranceEmployee assistance programWellness programAnnual air travel allowance for expatriatesRegular performance feedback and salary reviewsGlobal travel medical and accident insuranceReferral bonusesLearning and development opportunities including in-house training and coaching, professional certifications, and courses

Senior Data Science Manager45-50k AED per month plus benefitsAbu DhabiDiscover the OpportunitiesWe are hiring a Senior Data Science Manager to join a leading financial institution in Abu Dhabi. Our client works across diverse banking domains to leverage data assets and implement cutting edge technology solutions. As a key leader in the business services division, you will bridge the gap between technical teams and senior business stakeholders, translating complex organisational challenges into high impact machine learning solutions that drive measurable growth.Discover the roleIn this position you will lead the development and adoption of data science and artificial intelligence initiatives across critical banking sectors like retail, risk, credit, and operations. You will provide strategic and technical leadership to a dedicated team, ensuring that all models are robust, explainable, and ready for production. This role focuses on embedding advanced statistical insights directly into the core decision making processes of the business.Discover the responsibilitiesDesign and deploy advanced machine learning models to improve efficiency and risk outcomes across customer journeysApply supervised, unsupervised, and time series modeling techniques using structured and semi structured dataLead feature engineering, model selection, training, validation, and benchmarkingConduct experimentation using hypothesis testing and statistical inference to quantify model impactCollaborate with engineering teams to ensure models are production ready and maintained through standard continuous integration practicesMonitor model performance, stability, and data drift to ensure ongoing reliability and regulatory complianceDrive the adoption of data driven practices across business units by managing change initiatives and training programsManage and mentor a team of two to five direct reports, organising activities to deliver on strategic objectivesDiscover the requirementsMinimum 6 years of experience in data science with proven expertise in artificial intelligence and cloud based deploymentsStrong background in banking processes, operations, terminologies, and machine learning operationsExperience leading cross functional teams and agile squads in a regulated environmentBachelor degree in a quantitative field such as Computer Science, Artificial Intelligence, Statistics, or MathematicsMaster degree in a related field is highly preferredAdvanced technical skills in Python, generative AI, large language models, and cloud based pipelines using Azure or AWS

Company Description Open Innovation AI is a global technology company that specializes in developing advanced solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The platform is hardware-agnostic, optimized for various GPUs and accelerators hardware, and facilitates seamless integration and scalability for enterprise AI applications. Open Innovation AI focuses on optimizing and simplifying AI workload management and making AI technologies accessible to organizations of all sizes. With its innovative solutions, companies can reduce operational costs, accelerate time to value, and maximize their return on investment, ensuring that their AI strategies contribute directly to enhanced business outcomesRole Overview:We're hiring an Senior AI Engineer to build the applied AI that our agentic platform runs on. The job is to take what's working in LLMs and agent systems right now and turn it into features people can depend on. Some of it is straight engineering. Some of it is keeping up with a field that moves every week reading the new work, trying it, and shipping the parts that hold up.Role Responsibilities:Write clean, reliable code for the agentic and applied-AI features at the core of the productGet LLM capabilities into production: agents, orchestration flows, RAG, tool use, multiagent setupsTry new models, techniques, and frameworks. Prototype them, see if they hold up, bring the good ones inBuild the evals, benchmarks, and observability we need to actually know whether an agent is getting better or worseKeep AI workloads running in production automation, scaling, the unglamorous partsWork with product and other teams to turn what users need into specs you can build againstDebug, test, and tune the core product when it's slow or wrongReview code and help junior engineers level upKeep an eye on what's new and flag what's worth the team's attentionRequired experience & QualificationA degree in CS or engineering, or the equivalent experience we care more about what you've built.Strong Python or TypeScript. Either is fine.You've built applied AI or ML and shipped it to real users, not just in notebooks.Hands-on with LLMs and the current toolset: orchestration frameworks, agentic patterns, RAG, prompt engineering, fine-tuning, evals a solid subset of these.Some exposure to MLOps/LLMOps: deploying, scaling, and monitoring models in production.Real grounding in software design. You should be able to reason about architecture, not just wire up API calls.Comfortable with Git.You can make progress on a deadline and explain your thinking to the people around youNice to have:A strongly typed language like Go, Java, or C++Open-source contributions, papers, or applied researchThe habit of reading AI research and actually turning it into product changes

Sr. AI Engineer - (Airline Domain)📌 Role: Senior GenAI/Agentic AI Engineer📍 Location: Abu Dhabi (Onsite)Exp.: 7+ years.Note: Looking for immediate joiners or candidates who are currently serving their notice period. It is acceptable to consider candidates from outside of UAE (like India), provided they have a genuine interest in the opportunity and are willing to relocate.Key Skills - Strong exp. with AI/ML, Gen AI, Agentic AI, LLMs, Azure Databricks, Python, Regression, Azure & Airline Domain exp. is mandatory.🌟 Role OverviewWe are seeking a senior AI Engineer (7+ years) to design, build, and scale production-grade machine learning systems on Databricks (Azure stack). This role requires deep engineering rigor, not just model building—ideal candidates have a strong background in distributed systems and software engineering.🧠 Key ResponsibilitiesBuild and deploy end-to-end ML pipelines on Databricks (training → deployment → monitoring)Design, develop, and deploy machine learning and AI models on Azure Databricks.Productionize ML systems using MLOps best practices (CI/CD, automation, observability)Design scalable architectures using Azure ML, ADLS, Synapse, and Databricks LakehouseWork with large-scale datasets using Apache Spark (batch + real-time)Develop robust feature engineering and model serving pipelinesCollaborate cross-functionally to translate business problems into scalable AI solutions⚙️ Required Skills7+ years in AI / ML, GenAI, Agentic AI, Data rolesStrong expertise in: Python (must-have) + ML frameworks (PyTorch / TensorFlow / Scikit-learn)Databricks + Apache SparkAzure ecosystem (ADF, ADLS, Synapse, Azure ML)Strong grounding in:Distributed systemsModel deployment & production-grade ML systemsSoftware engineering (system design, testing, scalability)It is our policy to provide equal employment opportunities to all individuals based on job-related qualifications and ability to perform a job, without regard to age, gender, gender identity, sexual orientation, race, color, religion, creed, national origin, disability, genetic information, veteran status, citizenship or marital status, and to maintain a non-discriminatory environment free from intimidation, harassment or bias based upon these grounds.

Job PurposeAs a Data Scientist at SAAL.ai, you will apply machine learning and deep learning techniques to solve real-world business problems across numeric and text-based analytics. You will work across the full data science lifecycle—from data acquisition and feature engineering to model development and deployment—collaborating closely with engineering, product, and DevOps teams to deliver customer-centric AI solutions.Key ResponsibilitiesDevelop and deliver high-quality machine learning and deep learning models across a broad range of analytics use cases.Understand the practical scope and constraints of AI models within products and apply appropriate modeling techniques to ensure business relevance and technical robustness.Acquire data from diverse sources, performing data exploration and preprocessing on structured and unstructured datasets, conducting feature engineering, evaluating algorithms and architectures, and iteratively refining models to improve performance.Identify valuable data sources and automate data collection and preparation processes where possible.Build, fine-tune, and maintain machine learning pipelines, integrate algorithms and packages into the platform, and contribute new models to the platform marketplace.Collaborate closely with engineering and DevOps teams to support model deployment and operationalization.Contribute to applied research activities, propose data-driven solutions to business challenges, and work with product, support, and client-facing teams to ensure successful rollout of AI solutions into trials and general availability.Key SkillsStrong foundation in the theory and applied practice of machine learning and deep learning. Experience developing pipelines for structured and unstructured data.Hands-on experience with deep learning frameworks such as TensorFlow and PyTorch, and machine learning libraries such as scikit-learn.Proficiency in Python, with working knowledge of Java or R.Experience developing and exposing models through RESTful APIs and containerized deployments using Docker.Strong coding practices, including appropriate use of data structures and clean, maintainable code.Applied experience in NLP, including the use of transformer-based models and libraries such as Hugging Face, spaCy, or Gensim. Familiarity with SQL, Pandas, Apache Spark, and data processing at scale.Experience using ML lifecycle tools such as MLflow.Strong analytical thinking, communication skills, and the ability to present technical concepts clearly to internal and external stakeholders.Ability to work collaboratively across multidisciplinary teams.Desirable SkillsExperience working with large language models (LLMs), including offline deployment scenarios. Exposure to cloud-based environments (AWS, GCP, Azure). Experience with data visualization tools and collaborative environments such as Jupyter Notebooks and Git-based workflows. Domain experience with education or finance datasets is an advantage.

Project descriptionExpert-level engineer designing and building LLM applications and agentic workflows on Azure: RAG pipelines, multi-agent orchestration, MCP tool integration, evaluation, guardrails and cost control. Sets the GenAI engineering standards for the account and is accountable for solution quality against agreed evaluation thresholds.ResponsibilitiesBuild LLM applications and agent workflows with LangGraph / LangChain / Semantic Kernel on Azure OpenAI (AWS Bedrock / GCP Vertex a plus).Design RAG pipelines: document ingestion, chunking strategies, embeddings, vector search (Azure AI Search, FAISS, Pinecone), hybrid retrieval and re-ranking.Integrate enterprise tools and context via Model Context Protocol (MCP) servers and function / tool calling; design multi-agent systems.Engineer, version and test prompts; run offline and online evaluations (accuracy, grounding, hallucination rate) and fine-tuning where justified.Implement responsible-AI guardrails (content filters, PII redaction, policy checks) and LLM observability (tracing, token accounting, latency).Optimise token and inference cost (model routing, caching, batching); report against cost budgets.Document prompts, evaluation results and model choices; mentor engineers adopting GenAI patterns.SkillsMust haveBachelor's degree in Computer Science, Engineering, Information Systems or a related field, or equivalent practical experience.10+ years in software or data engineering, of which 3+ in machine learning / NLP and 2+ delivering production LLM applications (RAG, agents) on Azure OpenAI or equivalent.Expert Python; LangGraph, LangChain and/or Semantic Kernel; asynchronous, event-driven application design.Azure OpenAI, Azure AI Search, Azure AI Foundry / ML, Functions, Container Apps / AKS, Key Vault, Application Insights.RAG design: chunking, embeddings, vector databases (Azure AI Search, FAISS, Pinecone), hybrid search, re-ranking.MCP server / client development, function calling, multi-agent orchestration and state management.LLM evaluation (RAGAS, promptfoo, Azure AI evaluation or equivalent), fine-tuning (LoRA / PEFT) and model selection.Guardrails (Azure AI Content Safety, NeMo Guardrails or similar), LLM observability (LangSmith, OpenTelemetry), FinOps for inference.Nice to haveAWS Bedrock or GCP Vertex AI.Semantic caching, model distillation, small-language-model deployment.Azure AI Engineer Associate (AI-102) or equivalent.Experience in financial services, sovereign wealth / investment holding or other regulated enterprise environments.Experience working with distributed teams (onsite UAE with nearshore India / offshore Poland squads).LanguagesEnglish: C1 Advanced

About the RoleYou will be designing, developing and deploying scalable generative AI solutions while managing and maintaining LLM deployments for internal AI tools, ensuring high availability and efficiency for internal teams, and training and deploying deep learning and LLMs models to production.ResponsibilitiesDesign and implement robust RAG pipelines to integrate Large Language Models (LLMs) with proprietary data sources, ensuring high accuracy and low latency in responses.Architect and maintain complex Apache Airflow DAGs to automate data ingestion, cleaning, embedding generation, and model retraining workflows.Manage and optimize Vector Databases (e.g., Pinecone, Milvus, Weaviate) for efficient storage and retrieval of high-dimensional embeddings.Maintain and monitor internal LLM deployments (hosting, scaling, and versioning), ensuring 99% uptime, managing GPU resources and optimizing inference for internal AI usage.Train, fine-tune, and deploy deep learning and LLM models to production environments, managing the full MLOps lifecycle from experimentation to serving.QualificationsAt least Bachelor's Degree in Computer Science, Software Engineering, Data Science, AI or related field8+ years in Software Engineering overallSolid experience in building LLM applications and RAG pipelines, in productionProduction experience with Python and DBs.Optimizing GPU compute resources (vLLM, TensorRT).Open-source contributions to AI/MLOps libraries.Advanced model fine-tuning (LoRA, PEFT).Good foundation in linear algebra and statistics.Required SkillsProgramming: Python (Must), Java (nice to have: GO, Scala)AI/ML: PyTorch, TensorFlow, Hugging Face.GenAI and LLMs: RAG, LangChain, LlamaIndex, RAG pipeline design. Nice to have: PEFT/LoRA fine-tuning.Data Processing: Apache Airflow, DAGs. Nice to have: PySpark or Ray.Databases: Vector DBs (Pinecone/Milvus), SQL/NoSQL, embedding models and semantic searchMLOps: Docker, Kubernetes, vLLM, Cloud (AWS/Azure/GCP), CI/CD pipelines. Nice to have: GPU resource management and optimization (CUDA).

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 an MLOps Engineer you set the ML infrastructure and reliability strategy across AI71's platform, including how LLMs and other deep learning models are deployed, fine-tuned, and served at scale. You own architecture decisions across both SaaS and on-prem operating models, mentor engineers across teams, and drive multi-quarter ML infrastructure strategy. You are a force multiplier.What You'll Do:Define ML infrastructure architecture across the platform: model deployment strategy (vLLM, Triton, or TGI), pipeline engineering (MLflow or Kubeflow), and cloud-native infrastructure across major cloud platforms (AWS, Azure, or GCP)Set direction for ML system reliability: monitoring, latency / throughput / availability targets, and incident response across research and production environments.Mentor senior MLOps engineers; raise the operational bar across multiple teams.Drive cross-team initiatives that improve inference performance and cost-efficiency, including distributed training frameworks (DeepSpeed, FSDP, Accelerate).Partner with ML researchers, product, and engineering leadership on multi-quarter ML infrastructure strategy.Ensure ML infrastructure scales across managed SaaS and fully air-gapped on-prem deployments.What You'll Bring:10+ years of MLOps, ML infrastructure, or machine learning engineering with history of architectural ownership.Proven track record architecting large-scale model deployment (including LLMs) and ML infrastructure at scale.Deep cloud expertise across major cloud platforms (AWS, Azure, or GCP) and strong Python proficiencyMentorship record — engineers you have grown now operate independently at higher levels.Deep comfort architecting ML systems that run in both managed SaaS and on-premises / disconnected air-gapped environments.Kubernetes at architectural depth — GPU scheduling, multi-tenancy, operators, and the failure modes of distributed workloads on shared clusters.Strong communication, stakeholder management, and decision-making skills, with a passion for building diverse, inclusive engineering teams. Strong Preference:Ownership of production reliability at platform level: SLO definition, incident command, postmortem practice, and driving reliability improvements across teams rather than services.Architecture-level experience with distributed training and fine-tuning at scale (DeepSpeed, FSDP, Megatron-LM), including cluster design, checkpointing strategy, and failure recovery.Deep GPU systems knowledge: CUDA, NCCL, interconnect topology (NVLink, InfiniBand/RoCE), and diagnosing performance and communication problems across nodes.Model optimization strategy at portfolio level: quantization (FP8, AWQ, GPTQ), speculative decoding, with measurable cost or latency outcomes across multiple systems.Experience in regulated or security-constrained environments — compliance-driven architecture, model governance, lineage, audit, and secrets management.On-prem / air-gap ML delivery architecture experience at scale.Track record maturing MLOps practice in a growing organization: standards, platform abstractions, and paved paths that outlived your involvement.Bare-metal GPU cluster architecture, including scheduling (Slurm or Kubernetes) and hardware lifecycle in customer or owned data centers.Nice to Have:Conference speaking, technical writing, or industry thought leadership.Open-source contributions to inference, serving, or ML infrastructure projects, particularly maintainer-level involvement.C/C++ or CUDA kernel experience for performance-critical paths.Arabic language skills.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.

About MistralMistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.The RoleMistral is seeking a Technical Lead, Applied AI to drive the technical strategy, execution, and delivery of complex AI solutions for our enterprise customers. In this role, you will lead a project teams of Applied AI Engineers, ensuring the successful deployment of Mistral AI products and the development of high-impact, scalable AI use cases.You will act as the primary technical point of contact for our most strategic customers, guiding them through the entire lifecycle—from pre-sales to post-implementation—while collaborating closely with research, product, and engineering teams to shape the future of our offerings.As a Technical Lead, you will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral’s technological vision.What You Will Do Deliver as an IC the critical lines of codes of our complex projects, you’ll be hands-on and de-risk the critical parts of our complex projects. You’ll stay deeply involved in coding, reviewing, and optimizing AI solutions. Lead technical teams of Applied AI Engineers, providing mentorship, technical guidance, and best practices for deploying state-of-the-art GenAI applications across industries. Lead technical discussions during pre-sales, translating customer requirements into actionable solutions and communicating Mistral’s technological advantages to diverse stakeholders. Design and oversee the implementation of complex AI systems, including fine-tuning, RAG, agentic workflows, and custom LLM applications, ensuring alignment with Mistral’s product roadmap and open-source initiatives. Drive innovation by identifying emerging trends in AI, evaluating new tools and methodologies, and championing best practices for fine-tuning, inference, and deployment. Work closely with product managers, researchers, and engineers to ensure seamless integration of customer feedback into Mistral’s product development cycle. How We Work In Applied AI We care about people and outputs. What matters is what you ship, not the time you spend on it Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. Always ask why. The best solutions come from deep understanding, not from copying what worked before We say what we mean. Feedback is direct, timely, and given because we care. No politics. Low ego, high standards. We embrace an unstructured environment and find joy in it. What We're Looking For You are fluent in English. You hold a PhD or Master’s degree in AI, Machine Learning, Computer Science, or a related field. You have 7/8+ years of experience in AI/ML, with at least 2+ years in a technical leadership role (e.g., Tech Lead, Engineering Manager, or Solutions Architect) focused on AI products or enterprise solutions. You have a proven track record of leading teams to deliver complex AI projects, from prototyping to production, in industries such as tech, finance, healthcare, or industrial automation. You possess deep expertise in fine-tuning LLMs, advanced RAG, agentic systems, and deploying NLP applications at scale. You are proficient in Python, PyTorch, and modern AI frameworks (e.g., LangChain, Hugging Face). Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools is a plus. You have strong software engineering skills, including API design, backend/full-stack development, and system architecture. You excel in technical communication, with the ability to articulate complex concepts to both technical and non-technical audiences, including executives and engineers. You thrive in fast-paced, collaborative environments and are passionate about mentoring and growing technical talent. Ideally, you have: Contributed to open-source projects, particularly in the LLM or AI space. Experience in customer-facing roles (e.g., Solutions Architect, Customer Engineer, or Technical Product Manager) with a focus on enterprise AI adoption. A track record of driving technical strategy and influencing product direction based on customer needs and market opportunities. Why join us? You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you!What we offerWe offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.For the most up-to-date details on benefits available in your location, please refer to our Benefits page.Privacy PolicyYour privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.

Job Title:Senior AI EngineerJob Code: JPC-6691Location: Abu Dhabi, UAEContract: 12 MonthsExperience: 6+ YearsRole OverviewWe are looking for a Senior AI Engineer with strong hands-on experience in AI/ML engineering, LLM application development, Microsoft Azure AI technologies, and enterprise project delivery.The ideal candidate should be capable of taking AI solutions from design and development through production deployment, while also managing technology vendors and system integrators.Key ResponsibilitiesDesign, develop, and deploy production-ready AI and LLM-based solutions.Build and integrate applications using Azure AI Foundry, Azure OpenAI, Copilot Studio, and Power Platform.Lead AI and digital projects end-to-end, ensuring timely delivery within budget.Work closely with business and technical stakeholders to translate requirements into scalable AI solutions.Manage and evaluate technology vendors and system integrators, from RFP and technical evaluation through implementation and delivery.Ensure AI solutions meet enterprise standards for quality, scalability, security, and performance.Support AI solution architecture, implementation, testing, deployment, and continuous improvement.Qualifications & Experience Bachelor s or Master s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field.6+ years of experience in software engineering, data engineering, or ML/AI engineering.Minimum 2 years of hands-on experience building and deploying LLM-based applications.Proven experience delivering AI solutions into production within an enterprise environment, beyond proof-of-concept projects.Strong hands-on expertise with the Microsoft AI stack, including:Azure AI FoundryAzure OpenAIMicrosoft Copilot StudioMicrosoft Power PlatformProven project management and delivery experience, with the ability to lead AI/digital projects from initiation to completion.Experience managing technology vendors and system integrators, including RFP, evaluation, negotiation, implementation, and delivery.Experience in regulated industries such as financial services, pensions, insurance, or government is highly desirable. Preferred CertificationsMicrosoft Azure AI Engineer AssociateAzure Solutions ArchitectPMPPRINCE2Agile certificationsKey SkillsAI/ML | Generative AI | LLM | Azure OpenAI | Azure AI Foundry | Copilot Studio | Power Platform | AI Solution Development | Production Deployment | Project Management | Vendor Management | Enterprise AI

The Development & Engineering section is responsible for the development and management of in-house applications and data platforms, designing and implementing new solutions, and supporting business technologists.Required Technical ExperienceFinancial Domain AI: Track record in building generative AI applications for financial services, including trading, compliance, investment operations, research, or internal productivity use cases.LLM Fine-Tuning and Adaptation: Hands-on experience adapting foundation models using LoRA, QLoRA, PEFT, supervised fine-tuning, instruction tuning, preference tuning, and knowledge distillation.Custom Model Deployment: Experience deploying fine-tuned or custom models into production or controlled enterprise environments using containerized inference, GPU-enabled infrastructure, model registries, and serving frameworks.Model Evaluation and Benchmarking: Ability to compare base, fine-tuned, distilled, and RAG-based approaches using task-specific evaluations, quality metrics, latency, throughput, and cost benchmarks.Open-Source Model Ecosystem: Practical experience with open-weight models such as Llama, Mistral/Mixtral, Qwen, Gemma, Phi, or similar models.Collaborative Multi-Agent Systems: Knowledge of building collaborative, autonomous agent systems that communicate effectively and coordinate complex tasks, ideally leveraging Semantic Kernel or LangGraph.Continuous Innovation: Interest and engagement in recent AI research and developments across OpenAI, Microsoft, Anthropic, open-source AI, and model optimization techniques.Technical SkillsetAI and LLM Integration - Python expertise, leveraging large language models (OpenAI, Anthropic, Mistral, GCP) within AI pipelines. Experience with LangChain, LangGraph, and Semantic Kernel for orchestrating generative workflows and agentic behaviors.Cloud Platforms - Strong knowledge of Microsoft Azure, including Azure OpenAI, Azure Machine Learning, and Azure Cognitive Search. Familiarity with AWS is beneficial but optional.Generative AI Pipelines - Ability to create multi-step, retrieval-augmented generation (RAG) pipelines using Azure Cognitive Search to integrate structured and unstructured company data effectively.Agentic System Design - Experience developing autonomous AI agents that handle context-aware interactions, leveraging recent advancements from OpenAI’s Agents SDK or Microsoft’s Semantic Kernel framework.Memory and Context Management - Skilled in designing conversational memory and long-term context solutions using Azure Cognitive Search, semantic retrieval, and extended-context techniques, such as Claude's large-context models.DevOps and Deployment - Familiarity with CI/CD practices, containerization (Docker), orchestration (Kubernetes), and observability tools for deployment in regulated environments.Key ResponsibilitiesDevelop existing and new modules within core applications supporting the front office investment teams.Deliver new functionality, data capabilities, and modules, as well as smaller enhancements and fixes to existing modules.Engage with IT and business stakeholders and take ownership of deliverables.Understand business problems and offer technical solutions.Contribute to continuous improvement of coding standards, practices, and SDLC maturity.Work in partnership with quants and traders to help build, support, and maintain a quantitative trading and risk optimization platform.Improve productivity through automation, where applicable.Education & ExperienceMinimum 5 years’ experience in enterprise-grade developer roles.Minimum 2 years’ relevant experience in AI-specific applications in an enterprise context.Bachelor’s or master’s degree in computer science or a related discipline, or equivalent combined education and experience.

About The Institute Of Foundation ModelsThe Institute of Foundation Models (IFM) at MBZUAI is a research lab dedicated to meaningful foundation model research — building models from scratch, understanding them deeply, and publishing work that shapes the field. You’ll work alongside world-class researchers and engineers on problems that directly define the models we ship.The RoleJoin the PAN world model project — our effort to build world models: foundation models that simulate, predict, and interact with the physical world. As a Machine Learning Engineer, you’ll own the engineering backbone of PAN: large-scale video and simulation data pipelines, distributed training for diffusion transformers, game-engine simulation environments, and world-model integration into robotics — turning research ideas into reliable, scalable systems.What You'll DoBuild and maintain large-scale video and simulation data pipelines — collection, cleaning, annotation, and filtering — to support world model trainingDevelop and optimize distributed training systems for large-scale diffusion transformers and world modelsBuild interactive simulation environments (e.g., Unreal Engine, Blueprint-based gyms, game integrations) for training and evaluating world modelsIntegrate world action models / video action models into robotics systemsOptimize inference and serving for real-time interaction, and turn research prototypes into reliable, reproducible systemsWhat We're Looking For BSc or above in Machine Learning, Computer Science, Robotics, or a related field, or equivalent industry experienceHands-on experience with state-of-the-art video generative models and world models (e.g., Cosmos-3, LTX 2.3, Self-Forcing, Lingbot-World, or comparable systems)Deep expertise in at least one of the following areas:Full-stack data pipelines — large-scale video data pipelines and/or simulation data collection; annotation and filtering workflows for video / world model trainingModel training & infrastructure — training large-scale diffusion transformers on large GPU clustersRendering engines & simulation — Unreal Engine and Blueprint-based gym environments, game-engine integration, building interactive simulated environmentsWorld action models & robotics — world action models / video action models, action-conditioned video generation, world-model applications in roboticsStrong engineering expertise in deep learning frameworks such as PyTorch, with the ability to debug failures across the training/inference stack (memory issues, deadlocks, I/O bottlenecks)Highly proficient with modern AI coding agents and web-based coding tools (e.g., Claude Code, Codex, Cursor), and skilled at leveraging them to dramatically accelerate engineering workflowsNice to HaveExperience accelerating diffusion model inference (distillation, few-step generation, real-time interactive generation)Practical experience with web scraping and crawling frameworks (e.g., scrapy, playwright, selenium) for web-scale data curationExperience with Unreal Engine C++/Blueprint development or other game-engine programmingExperience deploying world models in robotics or embodied-AI settingsWe 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 The Institute Of Foundation ModelsWe are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.The RoleAs a Machine Learning Engineer at the Institute of Foundation Models, your primary responsibility is to develop and implement innovative machine learning models that address real-world challenges, pushing the boundaries of artificial intelligence research. You will collaborate with cross-functional teams to deploy scalable solutions, contributing to MBZUAI’s mission of driving impactful AI discoveries and positioning the institution as a leader in the global AI research community. Your expertise will be key in enhancing the performance of large-scale machine learning models, while supporting the development of transformative AI tools that can influence industries worldwide.Key ResponsibilitiesCollaborate with Research teams to understand technologies, adapting and integrating them into codebaseDevelop and implement systems to support the lifecycle of machine learning models, such as data preprocessing, pre-training, post-training, evaluation and so on, especially foundation modelsParticipate in or lead design reviews with peers and stakeholders to decide amongst available technologiesReview code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedbackTriage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and qualityContribute to research papers and represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovationPerform all other duties as reasonably directed by the line manager that are commensurate with these functional objectivesAcademic QualificationsMinimum: Bachelor’s degree or equivalent practical experience. Preferred: Master's degree or PhD in Computer Science or related technical fieldProfessional Experience - Minimum3 years of experience in software engineering, including experience with Machine Learning (ML) models, ML infrastructure, Natural Language Processing or Computer Vision2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting2 years of experience with data structures or algorithms in either an academic or industry setting2 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, or natural language processingExcellent problem-solving and troubleshooting skills to address complex technical challengesEffective communication and collaboration skills to work with cross functional teamsProfessional Experience - Preferred2 years of experience with improving performance during large scale data processingHands-on experience with LLM algorithms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)Excellent data analysis skillsWe 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.

Job PurposeAs an AVP– Data Science, you will design, build, and productionize advanced analytics and AI solutions that drive measurable business value. You will work closely with Data Product Owners, Data Analysts, BI Developers, and Data Quality Specialists to translate complex business challenges into data-driven models and experiments. Leveraging Python, SQL, and modern ML frameworks, you will develop scalable machine learning solutions that are explainable, governed, and aligned with organizational priorities.Roles And ResponsibilitiesTranslate business problems into data science projects by defining clear hypotheses, success metrics, and validation methods.Explore, clean, and transform structured and unstructured data using Python and SQL to prepare high-quality datasets for modeling.Design, train, and evaluate machine learning models using appropriate algorithms and statistical techniques (e.g., regression, classification, clustering, NLP, forecasting).Collaborate with engineering and platform teams to productionize AI models through reproducible pipelines and CI/CD workflows.Apply model explainability, fairness, and interpretability techniques (e.g., SHAP, LIME, feature importance) to ensure transparency and accountability.Support AI governance activities, including Model Risk Management (MRM) processes, ensuring compliance and responsible use of AI.Conduct and analyze A/B tests or controlled experiments to assess model and feature performance.Work with Data Product Owners to define business outcomes, monitor model performance post-deployment, and ensure continued relevance.Collaborate with Data Quality Specialists to ensure input data meets quality, lineage, and governance standards.Communicate results effectively through visualizations, storytelling, and presentations tailored to technical and non-technical audiences.Related Years Of Experience6+ years of experience in data science, applied machine learning, or advanced analytics.Proven experience delivering models that have been deployed and integrated into business processes or digital products.Experience working in agile, cross-functional teams with Product Owners, Data Analysts, and Data Engineers.Field Of ExperienceAnalytics, Data Science, Artificial IntelligenceTechnical And Interpersonal SkillsAdvanced proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM).Strong command of SQL for data extraction, transformation, and validation.Experience working in Databricks or equivalent data and ML platforms.Familiarity with deep learning frameworks (PyTorch, TensorFlow) and ML lifecycle tools (MLflow, Airflow, Docker, Kubernetes).Understanding of model explainability, ethics, fairness, and governance.Knowledge of AI governance processes and documentation standards, including Model Risk Management (MRM).Exposure to cloud platforms (Azure, Snowflake) and APIs for integrating AI models into applications.Familiarity with version control (Git/GitHub) and collaborative coding practices.Excellent communication skills to explain technical findings in clear business terms.Collaborative and curious, with a passion for continuous learning and innovation QUALIFICATION Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.Master’s degree or higher in Data Science, Machine Learning, or Applied Statistics is an advantage.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.

Avrioc is looking for a hands-on Data Scientist with strong expertise in Deep Learning, Natural Language Processing, Computer Vision, Speech Processing, Distributed Model Training, and GPU optimization. You will be responsible for developing, training, optimizing, and deploying AI models in production environments.Key ResponsibilitiesDevelop, train, validate, and optimize frontier AI models.Work on Multi-Modal large scale use cases.Inference and training recipe optimizations for GPU-based environments.Take models from ideation, training and inference scaling through production deployment.Collaborate with engineering and product teams to deliver scalable AI solutions in a research friendly setting.Must-Have Requirements4+ years of professional experience in AI:Model Training / Inference Optimization / Distributed Systems / Systems Programming / GPU ProgrammingStrong hands-on experience in Deep Learning model training.Strong programming skills in C/C++, Rust, Zig-lang etc.Strong experience with libtorch / PyTorch ecosystem.Hands-on experience with open-source inference engines vLLM, SGLang, Llama.cpp etc.Hands-on experience with CUDA, RoCM and their template libraries.Strong Linux and shell scripting experience.Good to HaveExperience with remote server interfaces such as AWS EC2.Experience with ROCm / AMD GPU technologies.TensorRT, ONNX, CoreML or Edge Device performance optimization.SLURM / OCI / Kubernetes / Docker / MLFlow.Experience with large-scale AI/ML systems.Key Skills: Machine Learning | Deep Learning | Python | PyTorch | OpenCV | Model Training | Computer Vision | Image Classification | Object Detection | Style Transfer | C/C++ | CUDA | GPU | Linux | AWS EC2 | ROCm

ABOUT THE COMPANYSWORD Group, being a leader in IT software solutions and services, has 4000 + IT/Digital & Software specialists present over 5 continents to accompany you in the growth of your organization in the digital age. SWORD is present in the MEA region through its offices in UAE, KSA, Cyprus, and Lebanon. As a leader in technological and digital transformation, SWORD Group has since the year 2000 acquired a solid reputation in software solutions and in complex IT & business project management. With SWORD you have the guarantee of strong close and qualified local commitment, the aim of which is to automate your business processes and optimize/maximize the usage of business data.ABOUT THE ROLEThe Senior AI Engineer delivers AI solutions that work in production and matter to the business. We are not looking for a pure technician: this role combines hands-on engineering with project delivery, vendor management and the judgement to know when a solution is good enough to ship. The role is based on-site in Abu Dhabi.SOFT SKILLS & BEHAVIORS These behaviors weigh as much as technical skills in our selection. The role sits at the interface between vendors, internal departments and leadership, and succeeds only if it keeps them working together. Bridge-builder: connects vendors, business departments, IT, InfoSec, procurement and leadership; translates between technical and business language and gets everyone aligned on one plan. Stakeholder management: builds trust with department heads and end users, manages expectations and handles disagreement without escalation.Challenger mindset: questions the status quo, vendor proposals and internal assumptions, constructively and with facts. Resilience: stays steady under pressure, shifting priorities and pushback; absorbs setbacks, slow approvals and failed pilots without losing momentum. Pragmatism: done beats perfect. Ships a working solution, measures it, improves it, and knows when to stop polishing and move on. Clear communication: explains trade-offs simply to non-technical audiences and senior management, in writing and in person. Ownership: takes a solution from idea to production and stays accountable for how it performs. Business sense: asks what problem is worth solving before choosing a model.Fluent English required; Arabic is a plus.ABOUT THE TEAM AND THE MISSIONYou will join a small, hands-on AI team that owns the organization's AI roadmap end to end: use-case discovery, delivery, governance and adoption.Our work spans pensions administration, investment operations and corporate functions. Current initiatives include agentic assistants deployed in Microsoft Teams, document intelligence, data reconciliation automation and an AI governance framework. Our stack is Microsoft-first (Azure AI Foundry, Azure OpenAI, M365 Copilot, Copilot Studio, Power Platform); UAE sovereign cloud is used only where hosted open-weight models are required. Security, data residency and regulatory compliance are non negotiable.KEY RESPONSIBILITIESSolution design and delivery Design and build end-to-end AI solutions: LLM applications, RAG pipelines, multi-agent workflows and ML models, from prototype to production. Translate business problems from pensions, investments and support functions into scoped technical solutions with measurable outcomes. Integrate AI services with enterprise systems (ERP, document repositories, Microsoft 365, internal APIs). Project delivery Run AI projects end to end: scope, plan, milestones, budget, risks and stakeholder reporting.Define the MVP and success criteria upfront, then ship iteratively instead of perfecting in isolation. Coordinate business owners, IT, InfoSec and procurement to keep projects moving. Vendor management Lead the technical evaluation of vendors and integrators: proposals, demos, PoCs and pricing. Challenge vendor claims, estimates and roadmaps; push back on over-engineered or overpriced solutions. Hold vendors accountable for delivery, quality and knowledge transfer to the internal team. Engineering and operations (LLMOps / MLOps) Build robust pipelines for data ingestion, embedding, retrieval, evaluation and deployment. Implement CI/CD, monitoring, logging, cost tracking and automated evaluation of model quality (accuracy, hallucination, latency). Own production reliability of deployed agents and models, including incident handling and continuous improvement. Governance, security and compliance Apply the organization's AI governance framework: risk assessment, human-in-the loop design, access control and audit trails.Ensure solutions meet data residency, information security and UAE regulatory requirements.Contribute to security reviews of AI vendors and third-party models. Technical leadership Set engineering standards, reusable components and reference architectures for the AI team.Mentor junior engineers and data scientists through code reviews and pairing. Evaluate new models, platforms and vendors, and present clear recommendations to leadership.QUALIFICATIONS & EXPERIENCE Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Engineering or a related field. 6+ years in software, data or ML engineering, including at least 2 years building and deploying LLM-based applications. Proven track record of shipping AI solutions to production in an enterprise environment, not only proofs of concept. Strong hands-on experience with the Microsoft AI stack (Azure AI Foundry / Azure OpenAI, Copilot Studio, Power Platform). Demonstrated project management experience: has led AI or digital projects end to end, on time and within budget.Hands-on experience managing and challenging technology vendors and system integrators, from RFP and evaluation through delivery.Experience in regulated industries (financial services, pensions, insurance, government) is a strong plus. Certifications are a plus: Azure AI Engineer Associate, Azure Solutions Architect, PMP, PRINCE2 or AgileTECHNICAL SKILLSRequired Depth on the below Microsoft stack matters more than breadth across frameworks:Microsoft AI StackMust-haveAzure AI FoundryAzure OpenAIAzure AI SearchCopilot StudioPower AutomateNice-to-haveM365 Copilot extensibilityMicrosoft GraphMicrosoft FabricGenAI & LLMsMust-havePrompt engineeringRAGEmbeddingsTool callingAgents using Semantic Kernel or Microsoft Agent FrameworkNice-to-haveFine-tuningOpen-weight model hostingModel Context Protocol (MCP)ProgrammingMust-havePython, production-qualitySQLREST APIsNice-to-haveC#TypeScriptMachine LearningMust-haveClassical MLscikit-learnXGBoostEvaluation methodologyNice-to-haveTime seriesAzure AI Document IntelligenceMLOps / DevOpsMust-haveAzure DevOps or GitHubCI/CDDockerMonitoring and evaluation of LLM applicationsNice-to-haveBicep or TerraformAKSMLflowDataMust-haveData pipelinesData qualityStructured and unstructured dataNice-to-haveDatabricksOracleSecurityMust-haveEntra IDKey VaultPrompt-injection mitigationNice-to-haveAI red-teamingMicrosoft Purview________________________________________Why working at SWORD ?Enjoy a Hybrid or Remote Work EnvironmentBeing part of a Dynamic and Supportive Environment focused on Employee WellnessGain International Exposure and Multicultural ExperienceLearn from a Diversified Team of experts across the worldParticipate in major projects with industry leadersHave the possibility to relocate to a regional office within SWORD Group

Job DescriptionThis 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.Job DetailsRole Level: Mid-Level Work Type: Full-Time Country: United Arab Emirates City: Abu Dhabi Company Website: https://candergroup.com Job Function: Data Science & AI Company Industry/Sector: OtherWhat We OfferAbout The CompanySearching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.ReportDisclaimer: talentmate.com is only a platform to bring jobseekers & employers together. Applicants are advised to research the bonafides of the prospective employer independently. 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Role Overview:We are looking for an NLP Engineer to lead the design, development, and deployment of production-grade NLP and Generative AI solutions. You will own complex AI initiatives end-to-end, drive technical architecture, and help shape our NLP/LLM engineering practices.Key ResponsibilitiesLead the design and development of scalable NLP and LLM-powered solutionsArchitect and optimize RAG pipelines, embeddings, vector search, and LLM applicationsFine-tune, evaluate, and optimize transformer-based models and LLMs for production useBuild robust data, training, inference, and model-evaluation pipelinesDrive improvements in model quality, latency, scalability, reliability, and costEstablish best practices for experimentation, evaluation, deployment, and monitoring of AI systemsTranslate complex business requirements into effective AI/ML solutionsCollaborate closely with ML engineers, software engineers, product teams, and stakeholdersMentor engineers and provide technical leadership across NLP/Generative AI initiativesStay current with emerging research, models, frameworks, and best practices in NLP and Generative AIRequirements5+ years of professional experience in NLP, Machine Learning, or Generative AIStrong Python programming and hands-on experience with PyTorch, TensorFlow, Hugging Face, or equivalent frameworksDeep understanding of NLP, transformers, LLMs, embeddings, RAG, fine-tuning, and model evaluationProven experience taking AI/ML solutions from experimentation to productionStrong software engineering and system-design skills, including APIs, data pipelines, and scalable architecturesStrong analytical and problem-solving skills with the ability to independently drive technical initiativesPreferredExperience with PEFT/LoRA, quantization, inference optimization, or distributed model trainingExperience with vector databases, MLOps, cloud platforms, and AI evaluation/observability frameworksExperience building AI agents, multimodal systems, or other advanced Generative AI applicationsContributions to open-source projects, research, publications, or applied AI/ML innovation

Job OverviewServe as a dedicated Data Scientist within the specialized Miral Destination AI team, working exclusively on initiatives that advance Miral Destination. Analyze Miral Destination data, develop and fine-tune AI and machine learning models, and generate actionable insights that directly support Miral Destination’s business objectives. Partner closely with the ML Ops Engineer to move models from experimentation into production, and report to the Senior Manager AI to ensure all work aligns with the destination’s AI roadmap and priorities.Job ScopeAnalyze Miral Destination datasets to uncover patterns, trends, and opportunities that inform destination decisionsDevelop, train, and fine-tune AI and machine learning models tailored to Miral Destination use casesBuild RAG pipelines, vector databases, and LLM-based solutions on Databricks for Miral Destination applicationsGenerate clear, actionable insights and visualizations that directly support Miral Destination business objectivesCollaborate with the ML Ops Engineer to operationalize models, ensuring they are production-ready and monitoredTranslate requirements from Miral Destination stakeholders, via the Senior Manager AI, into data science deliverablesAlign all work with the enterprise AI architecture, governance, security, and data management standards set by the AI & Data organizationUphold data quality, model performance, governance, and responsible-AI standards across all Miral Destination workCollaborate with AI, Data Engineering, BI, and Enterprise Data teams across DTD to leverage shared capabilities, reusable assets, common platforms, and best practices.Job Essentials3–5 years in Data Science, Machine Learning, or Advanced AnalyticsDemonstrated experience taking models from analysis through to deploymentStrong data analysis, statistical modeling, and machine learning skillsHands-on experience with Python and Databricks (incl. MLflow)AI model development, fine-tuning, and evaluationUnderstanding of RAG, LLMs, and vector databasesData storytelling – turning analysis into clear, actionable insightsCollaboration with ML Ops to deploy and monitor modelsEducationBachelor’s or Master’s in Data Science, AI, Statistics, Computer Science, or related field

We're excited to be partnering with a leading institution in the UAE that is building a digital-first, AI-enabled financial services organisation from the ground up. This is a unique opportunity to join at an early stage and help shape the technology, data, and AI foundations of a business with significant institutional backing and long-term ambition. If you're passionate about artificial intelligence, machine learning, and building production-grade AI solutions, we'd love to hear from you. As an AI Engineer, you'll play a key role in building the models, agents, and GenAI services that power an AI-native bank. Working closely with Data Engineering, AI Product Management, and Platform teams, you'll develop and deploy intelligent solutions that drive automation, customer experiences, and operational efficiency at scale. Responsibilities• Design, build, and deploy machine learning models, GenAI services, and AI-powered applications in production environments. • Develop agentic workflows, orchestration solutions, and AI services integrated into banking processes. • Build retrieval-augmented generation (RAG) pipelines, vector search capabilities, and grounding services. • Create and maintain evaluation frameworks, behavioural regression testing, and monitoring processes for AI systems. • Collaborate with product and engineering teams to identify and deliver high-value AI use cases. • Apply modern software engineering practices including CI/CD, testing, observability, and AI-assisted development. Requirements• 6+ years of experience building and deploying AI, machine learning, or GenAI solutions in production. • Proven experience delivering LLM, RAG, or agentic AI applications beyond proof-of-concept stage. • Strong experience with cloud AI/ML platforms such as Databricks, Azure, AWS, and MLflow. • Hands-on experience with Python, Kubernetes, CI/CD pipelines, and modern software development practices. • Experience building AI evaluation frameworks, monitoring solutions, and production-grade observability. • Experience within banking, fintech, or another highly regulated environment is advantageous. Ready to join us? Apply today and take the next step in your career.

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.