Data Scientist (Azure AI Engineer)

Job Description

Job Description — Full Stack Data Scientist (Azure AI Engineer)

Job Type: Contract

Job Summary

The ideal candidate has shipped production systems with measurable business impact and can operate them reliably through strong MLOps/LLMOps practices.

1. 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.

1. 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:
• LangChain for tool/function orchestration, retrieval, and integrations
• LangGraph for stateful, multi-step, resilient agent workflows
• Microsoft Agent Framework for enterprise-grade agent patterns and integrations
• 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.

1. 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.

1. 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.

1. 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

1. 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.

1. 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.

1. 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.

1. 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

• AI-102: Microsoft Certified – Azure AI Engineer Associate
• DP-100: Microsoft Certified – Azure Data Scientist Associate

• 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.
• Data: Strong SQL; experience with structured + unstructured data.

• Demonstrated end-to-end delivery of AI applications in production (build → deploy → operate), with measurable impact.

• 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.

• 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.