Lead AI Analytics Engineer

About the role
To build AI solutions on our Databricks and Fabric platforms for US healthcare operations such as claims, payment integrity, care management and contact centre, and help our BI and ETL engineers grow into AI work. This is a hands-on role.
Location: Bangalore
Experience: 7 -12 years in data, software or analytics or ML engineering, with at least 2 years building GenAI solutions that are live in production
What you'll do
Build and run GenAI and agent-based solutions end to end, from design through deployment and monitoring, using Azure OpenAI, AI Foundry and Databricks Mosaic AI.
Deliver analytics use cases such as conversational analytics and text-to-SQL, document intelligence, automated insights and data-quality checks.
Build RAG pipelines and vector search over Databricks, SharePoint and enterprise APIs.
Set up evaluation (groundedness, hallucination, retrieval quality, regression) and monitoring (latency, tokens, cost, errors) for everything you ship.
Keep solutions compliant with HIPAA and PHI requirements, including masking, access-controlled retrieval and prompt-injection protection.
Work with operations leaders to find use cases worth building and prove their value.
Mentor BI and ETL engineers and set up reusable standards for the team.
Must have
Strong Python and SQL
Azure OpenAI / AI Foundry, RAG, vector search, agents and tool calling
Hands-on Databricks and/or Microsoft Fabric
Experience evaluating and monitoring LLM solutions in production
CI/CD and deployment on Azure
Good to have
Understanding of Healthcare payer or provider data
Semantic Kernel, LangGraph or AutoGen; MCP
Copilot Studio / Microsoft Graph
Classical ML and MLflow
AI-102 or Databricks GenAI certification
In your first 12 months
2 - 3 AI use cases in production with measurable impact
An evaluation and monitoring standard adopted by the team
3 - 5 internal engineers contributing to AI delivery
Regards,
Chetan G
chetan.gurudev@sagility.com

Work arrangement
Hybrid