Full Stack Data Scientist

Job description / Role

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.

Key responsibilities

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.
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
   • 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 and 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 and 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 and unstructured data
   ?o vector and 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.
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, and 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 and 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 or 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 proof of concept to production and post-launch iteration.