Full Stack Data Scientist (Azure AI Engineer)Location: DubaiExperience: 8+ years (Data Science / AI Engineering / Applied ML)Job Type: Full-time
Job Summary : We are looking for a highly capable Full Stack Data Scientist / Azure AI Engineer who can build end-to-end AI products: data + ML/DL/CV models + Agentic workflows + APIs + UI + scalable deployment on Kubernetes (AKS). The role requires deep expertise in the Azure AI ecosystem (Azure Machine Learning, Azure AI Foundry, Azure AI Search) and strong handson experience building AI agents using LangChain, LangGraph, and/or Microsoft Agent Framework, with Langfuse for tracing, evaluation, and observability. The ideal candidate has shipped production systems with measurable business impact and can operate them reliably through strong MLOps/LLMOps practices.
Key Responsibilities1) End-to-End AI Product Delivery • Own delivery from problem definition → architecture → development → deployment → monitoring → iterative improvements. • Translate business needs into robust AI solutions with clear KPIs, timelines, and measurable outcomes. • Build AI applications that are secure, scalable, maintainable, and production ready.
2) AI Agents & Agentic Workflows (Must-Have)• Design, implement, and orchestrate AI agents capable of planning, tool use, function calling, retrieval, and multi-step execution. • Build agent systems using: o LangChain for tool/function orchestration, retrieval, and integrations o LangGraph for stateful, multi-step, resilient agent workflows o Microsoft Agent Framework for enterprise-grade agent patterns and integrations Group IT • Implement agent patterns: routing, task decomposition, multi-agent collaboration, memory, verification, retries/fallbacks, and human-in-the-loop approvals. • Apply security & safety: prompt-injection defenses, tool permissioning, grounding/citations, policy checks, and audit logs.
3) LLMOps / Observability / Evaluation (Langfuse) • Implement Langfuse (or equivalent) for: o prompt and trace logging, latency/cost monitoring o dataset-based evaluation, regression testing, and quality gates o feedback loops and continuous improvement of prompts/agents • Establish evaluation frameworks for RAG/agents: retrieval metrics, answer quality, hallucination checks, and guardrail effectiveness.
4) Azure Machine Learning & MLOps (Must-Have) • Build/operate ML workflows using Azure Machine Learning: o training jobs, compute, environments, pipelines, MLflow tracking o model registry and promotion, managed online endpoints • Implement CI/CD for model + application releases and MLOps practices: versioning, reproducibility, automated testing, and retraining triggers.
5) Azure AI Foundry & Azure AI Search (Must-Have) • Build GenAI solutions using Azure AI Foundry (prompt flows/orchestration, deployment integration, evaluation workflows). • Implement RAG pipelines using Azure AI Search: o ingestion/indexing of structured & unstructured data o vector + hybrid search, semantic ranking (where applicable), filtering, and relevance tuning o citations, metadata-based access control, and indexing automation
6) ML/DL & Computer Vision (Strong Requirement) • Develop and deploy strong ML/DL solutions including Computer Vision: o classification, detection, segmentation, OCR/document understanding, anomaly/defect detection • Conduct experimentation, tuning, and optimization (performance, robustness, cost). • Productionize CV pipelines with monitoring and continuous improvement. Group IT
7) Backend/API Engineering (FastAPI + Node.js) • Build production APIs for models and agents using FastAPI (Python) (async, OpenAPI/Swagger, auth, middleware, validation). • Build service orchestration and integrations using Node.js where appropriate. • Implement secure API patterns: authentication/authorization (Azure AD/RBAC patterns), rate-limiting, caching, and error handling. 8) Frontend Engineering (React) • Build modern UIs in React for AI applications (agent chat UI, dashboards, workflow screens). • Support streaming responses, citations, session memory, feedback capture, and user analytics.
9) Kubernetes/AKS Deployment & Operations • Containerize services using Docker and deploy on Kubernetes (AKS preferred). • Implement scaling, rollouts, secrets/config management, ingress, and reliability patterns. • Set up monitoring/telemetry using Azure Monitor/App Insights (or equivalent), alerts, and runbooks.
Required Skills and Qualifications Mandatory Certifications (Must) • AI-102: Microsoft Certified – Azure AI Engineer Associate • DP-100: Microsoft Certified – Azure Data Scientist Associate 
Core Technical Skills • Agents/Frameworks: Strong hands-on experience with LangChain, LangGraph, and Microsoft Agent Framework• LLMOps: Strong experience with Langfuse for tracing/evaluation/monitoring (or equivalent tooling, with Langfuse preferred). • Azure: Azure ML, Azure AI Foundry, Azure AI Search; plus Key Vault, Storage, App Insights/Monitor as needed.• Programming: Strong Python; API development with FastAPI; Node.js for services/integrations. • Frontend: React for production UI development.• ML/DL/CV: Proven hands-on depth in ML/DL and Computer Vision. • Deployment: Docker + Kubernetes/AKS. Group IT• Data: Strong SQL; experience with structured + unstructured data.]
Proven Experience (Non-Negotiable)• Demonstrated end-to-end delivery of AI applications in production (build → deploy → operate), with measurable impact.
 Preferred Qualifications • Experience in real estate / construction domain AI use cases (valuation, forecasting, risk, customer support automation). • Exposure to graph databases (e.g., Neo4j) and vector search/vector databases for AI applications. • Extra certifications (nice-to-have): Azure Fundamentals (AZ-900), Azure Developer (AZ-204), Kubernetes (CKA/CKAD), Databricks ML.
What Success Looks Like (Outcomes) • Delivered production-grade AI solutions end-to-end: data → model → agentic workflow → API → UI → AKS deployment → monitoring. • Established strong LLMOps with Langfuse: traceability, evaluation, cost controls, and reliability improvements. • Built reliable, secure, observable systems with measurable business impact (time saved, accuracy gains, automation rate, cost reduction). • Demonstrated strong ownership from POC to production and post-launch iteration.