Full Stack Data Scientist (Azure AI Engineer)

Overview

Full Stack Data Scientist (Azure AI Engineer) Location : Dubai Experience : 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 hands-on 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.

Responsibilities

• 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.
• 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.
• LLMOps / Observability / Evaluation (Langfuse)
• Implement Langfuse (or equivalent) for : prompt and trace logging, latency / cost monitoring
• dataset-based evaluation, regression testing, and quality gates
• feedback loops and continuous improvement of prompts / agents
• Establish evaluation frameworks for RAG / agents : retrieval metrics, answer quality, hallucination checks, and guardrail effectiveness.
• Azure Machine Learning & MLOps (Must-Have)
• Build / operate ML workflows using Azure Machine Learning : training jobs, compute, environments, pipelines, MLflow tracking
• model registry and promotion, managed online endpoints
• Implement CI / CD for model + application releases and MLOps practices : versioning, reproducibility, automated testing, and retraining triggers.
• 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 : ingestion / indexing of structured & unstructured data
• vector + hybrid search, semantic ranking (where applicable), filtering, and relevance tuning
• 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 : 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.
• 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.
• 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.
• 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.

Qualifications

Preferred Qualifications

What Success Looks Like

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