About the role : This is not an ETL role. We are looking for a hands-on lead to build data applications and AI assistants that run natively on Databricks for enterprise clients: query and lookup screens built with Databricks Apps, natural-language querying with AI/BI Genie, and retrieval-augmented assistants over large document repositories using Vector Search and Model Serving.

You will own the solution end to end: architecture, build, security model, client conversations, and a small delivery team. If your Databricks experience is mostly pipelines and Medallion layers, this role will not be a fit. If you have shipped Databricks Apps and Genie Spaces to real users and built production RAG, we want to talk.

What you will do

- Lead the architecture and delivery of Databricks-native applications: Databricks Apps (Python or React/TypeScript), Databricks SQL Warehouse, Unity Catalog, Volumes
- Design and tune AI/BI Genie Spaces for accurate natural-language querying over governed data
- Build document intelligence pipelines: extraction and OCR at scale, chunking, embeddings, Databricks Vector Search indexes with incremental refresh
- Implement agents with the Mosaic AI Agent Framework and Model Serving, including hybrid retrieval across structured and unstructured data with citations
- Enforce security by design: SSO, Unity Catalog RBAC,



permission filtering before LLM grounding, audit logging
- Run requirement workshops with client stakeholders, estimate effort, and plan and track delivery milestones
- Mentor engineers and review their work

What we are looking for

- 8+ years in data or software engineering, including 3+ years hands-on with Databricks and Unity Catalog
- At least one Databricks App shipped to production users
- Hands-on experience configuring and tuning AI/BI Genie Spaces
- Production RAG delivered end to end: chunking strategy, metadata filtering, vector index maintenance, retrieval quality evaluation
- Databricks Vector Search, Model Serving, Foundation Model APIs, Mosaic AI Agent Framework
- Strong Python and PySpark; solid SQL; Streamlit, Dash, or React/TypeScript
- Document processing at scale, including OCR of scanned material
- Experience leading a delivery team and working directly with enterprise clients on requirements and estimates
- Transparent written and spoken English; comfortable presenting architecture to client leadership

Nice to have

- Lakebase, Genie App Builder, Appkit
- Delivery experience in regulated or security-sensitive enterprise environments
- Working with legacy Oracle data models
- Middle East client exposure

📌 Lead Databricks AI Engineer (India)
🏢 Namasys Analytics
📍 India