Your AI career in the UAE starts here

Location: Dubai, United Arab EmiratesEmployment: Full-time, on-siteCompany DescriptionMajlis CRM is an AI-powered real estate CRM built for brokerages in the UAE. We help real estate teams manage leads, match buyers with properties, automate follow-ups, and improve client engagement. We are building AI into everyday brokerage workflows, giving agents more time to build relationships and close deals.Role DescriptionWe are looking for an AI Engineer to design, build, and deploy production-ready AI features across Majlis CRM. You will work closely with our founders, product team, and developers to turn real estate workflows into reliable AI-powered experiences.Your work will include conversational assistants, AI agents, intelligent property search, lead qualification, and call and meeting intelligence. You should be comfortable taking a feature from experimentation through deployment, measuring its performance, and improving it based on real customer usage.Key ResponsibilitiesBuild and integrate features using leading language and multimodal models from OpenAI, Anthropic, Google, and relevant open-weight model providers.Develop AI agents that retrieve information, use tools, update CRM records, create tasks, and coordinate follow-ups with appropriate permissions and human approval.Build retrieval-augmented generation (RAG) pipelines, including document ingestion, parsing, chunking, embeddings, hybrid search, metadata filtering, and reranking.Develop natural-language property search and buyer-property matching that combine semantic understanding with accurate structured filters such as budget, location, bedrooms, and availability.Extract structured information from enquiries, conversations, and documents to support lead classification, qualification, routing, and CRM updates.Build call and meeting intelligence workflows covering transcription, speaker diarization, summaries, action items, and follow-up tasks.Design prompts, context management, conversation memory, structured outputs, and tool integrations for reliable AI behavior.Establish evaluation datasets and automated tests to measure retrieval quality, factual accuracy, tool execution, and business outcomes.Monitor production AI systems and optimize response time, token usage, cost, and reliability through model selection, caching, routing, retries, and fallbacks.Implement access controls, tenant isolation, audit trails, and safeguards against prompt injection and unauthorized data access.Evaluate new models and tools, adopting them when they offer measurable improvements for the product.Required QualificationsStrong software engineering fundamentals and proficiency in Python, with experience building backend services, REST APIs, asynchronous workflows, and database integrations.Hands-on experience shipping and maintaining LLM-powered applications beyond prototypes or chatbot demos.Practical experience with one or more leading model APIs, such as OpenAI, Anthropic Claude, or Google Gemini, and the ability to compare models for quality, latency, cost, and privacy requirements.Strong understanding of prompt and context engineering, function calling, structured outputs, schema validation, and model limitations.Experience building agent workflows using direct model SDKs or frameworks such as LangGraph, OpenAI Agents SDK, or equivalent.Experience with RAG, embedding models, vector search, and relational databases, using tools such as PostgreSQL with pgvector, Qdrant, or Pinecone.Ability to decide when to use SQL queries, conventional application logic, retrieval, or an LLM to solve a problem reliably.Experience evaluating and debugging AI systems, including tracing model and tool calls, analyzing failures, and maintaining regression tests.Familiarity with Git, Docker, CI/CD, automated testing, and deployment on a major cloud platform.Strong problem-solving skills, clear communication, and the ability to own features from design through production.What We ValueWe value engineers who can demonstrate working AI products, explain their technical decisions, and show how they measured and improved results. Experience with every listed tool is not required. Strong engineering judgment, practical delivery, and the ability to learn quickly matter most.

Join one of the world's leading multi-strategy investment platforms as a Quant Researcher focused on Machine Learning and AI. This role sits within a highly successful systematic investment team that is investing heavily in next-generation research capabilities and the application of cutting-edge machine learning techniques to financial markets.The team is seeking exceptional researchers who can bring expertise from frontier AI, deep learning, large-scale modelling, and modern machine learning research into a fast-paced environment where ideas are rapidly tested and deployed. This is an opportunity to work on some of the most challenging prediction problems in the world while having direct impact on investment performance.ResponsibilitiesDevelop and implement machine learning models to identify predictive signals across global marketsResearch and apply state-of-the-art deep learning techniques for alpha generationDesign and test neural network architectures for financial forecasting and pattern recognitionWork with large, complex, and alternative datasets to uncover unique sources of investment insightConduct rigorous research, experimentation, and statistical validation of trading signalsCollaborate closely with Portfolio Managers, Quant Researchers, and Data ScientistsBuild scalable research infrastructure and production-ready modelling pipelinesStay at the forefront of developments in AI, machine learning, and quantitative researchContribute to the development of systematic investment strategies using modern ML techniquesRequirementsPhD or Master's degree in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative disciplineStrong background in machine learning research and modellingExperience building and deploying deep learning models in real-world environmentsExpertise in areas such as neural networks, representation learning, transformers, foundation models, reinforcement learning, probabilistic modelling, or large-scale optimisationExcellent programming skills in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlowStrong mathematical and statistical foundationsDemonstrated ability to conduct independent research and solve complex problemsExperience from leading AI labs, deep technology companies, research institutions, or high-performance machine learning environments is highly desirablePreferred BackgroundsWe are particularly interested in candidates from:Leading AI research labsFrontier foundation model teamsDeep learning and applied AI organisationsResearch-focused technology companiesQuantitative research groupsHigh-performance computing and data science environmentsAcademic researchers with strong publication records in machine learning or artificial intelligenceWhat You'll GetOpportunity to apply cutting-edge AI research to real-world investment decisionsAccess to significant computational, data, and research resourcesHighly collaborative environment alongside world-class investors and researchersCompetitive compensation package with substantial upside potentialDirect impact on investment outcomes and business growthRelocation support to Dubai where applicableExposure to one of the most intellectually challenging applications of machine learningThis position is ideal for researchers who are passionate about solving difficult prediction problems, building innovative machine learning systems, and applying frontier AI techniques in a highly competitive and rewarding environment. Desired Skills and ExperienceSeeking elite ML Researchers and PhDs to develop deep learning models for alpha generation within a highly capitalised, technology-driven investment team.

General Summary:The Sr. Digital Delivery Specialist at EMSTEEL designs, builds and delivers advanced AI and machine-learning solutions that create measurable business value across the Group’s steel and building-materials operations. The role sits within the Digital Centre of Excellence and focuses on Generative AI, Agentic AI, Computer Vision, AI Digital Twins and productionizing models through modern MLOps practices applied to industrial use cases such as process optimization and predictive maintenance. Working end-to-end from use-case discovery and experimentation to deployment, monitoring and adoption. Essential Duties and Responsibilities:Partner with business stakeholders and SMEs to identify, qualify and prioritize high-impact AI/ML use cases aligned to EMSTEEL’s strategic objectives.Design, develop and deploy Generative AI solutions (LLM-based assistants, RAG pipelines, document intelligence, summarization and content generation) tailored to enterprise needs.Build Agentic AI systems — autonomous and multi-agent workflows that reason, plan, use tools and orchestrate tasks, including Agent-to-Agent (A2A) collaboration and protocols across enterprise applications.Deliver industrial AI use cases across the value chain — process optimization, predictive maintenance, quality prediction, energy and yield optimization, and anomaly detection for steel and building-materials operations.Develop and operationalize AI Digital Twin solutions that simulate, monitor and optimize plant assets and production processes in real time.Build Computer Vision solutions for defect detection, surface-quality inspection, safety monitoring and process automation on the shop floor.Develop, validate and optimize classical and deep-learning models for prediction, optimization, anomaly detection and process control.Implement robust MLOps practices: CI/CD for models, automated pipelines, feature stores, model registry, versioning, monitoring, drift detection and retraining.Engineer scalable data and AI pipelines on Databricks and integrate solutions across the Microsoft (Azure) stack and Dataiku.Own end-to-end AI project delivery — from proof-of-concept to production — ensuring quality, security, performance and on-time delivery.Drive AI adoption by embedding solutions in downstream applications, updating SOPs, enabling users and delivering relevant training and change management.Establish responsible-AI, governance, evaluation and guardrail practices for GenAI and agentic solutions (accuracy, safety, bias, data privacy and cost control).Communicate findings, model behavior and business impact clearly to both technical and non-technical audiences.Additional Duties and ResponsibilitiesContribute to the AI reference architecture, reusable components and internal best-practice standards.Stay current with emerging GenAI/agentic frameworks, foundation models and tooling, and pilot promising innovations.Mentor junior Sr. Digital Delivery Specialists and analysts and support a culture of experimentation and continuous learning.Knowledge, Skills and/or Abilities RequiredStrong hands-on experience in machine learning, deep learning and statistical modelling using Python (and SQL).Proven expertise in Generative AI LLMs, prompt engineering, RAG, embeddings, vector databases and fine-tuning.Experience building Agentic AI solutions and multi-agent / Agent-to-Agent (A2A) systems using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or similar.Experience delivering industrial AI use cases process optimization, predictive maintenance and AI digital twins.Hands-on Computer Vision experience (OpenCV, PyTorch/TensorFlow, object detection, segmentation, defect/anomaly detection).Solid MLOps capability MLflow, model deployment, containerization with Docker/Kubernetes, CI/CD, GitHub / GitHub Actions and model monitoring.Proficiency with Databricks (Spark, Delta Lake, Unity Catalog, notebooks) for large-scale data and ML workloads.Working knowledge of the Microsoft/Azure AI stack (Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Fabric/Synapse) and Dataiku.Ability to translate business problems into technical solutions and communicate results effectively.Strong problem-solving, stakeholder-management and cross-functional collaboration skills.Minimum Requirements (Must Have):EducationBachelor’s / Master’s in Computer Science, Data Science, AI, Statistics, Engineering, Physics or Mathematics.Experience4+ years in data science / machine learning delivery.Demonstrated delivery of GenAI or agentic solutions to production.2+ years implementing cutting-edge AI/GenAI technologies.Exposure to manufacturing, heavy industry or steel operations.Experience with responsible-AI governance and LLMOps.Training / CertificationsCertifications in Machine Learning / AI, Databricks, Microsoft Azure AI or Dataiku.

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.

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.

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