Agentic AI Architect with Azure Databricks

Top 5 must-have areas

1. Azure Databricks / Lakehouse

2. Strong Data Engineering + PySpark/SQL

3. Agentic AI / AI Agents

4. GenAI, RAG, Azure OpenAI, LangGraph/MCP

5. Enterprise architecture & technical leadership

• Experience: 16–20 years; ideal profile has 15+ years in Data & Analytics.

• Location: Chicago, IL — Hybrid, 3 days/week in office.

• Core requirement: Strong Azure + Databricks + Data Engineering + Agentic AI experience.

• Data Platform: Azure Databricks, Delta Lake, Unity Catalog, ADLS Gen2, ADF, Databricks Workflows/DLT.

• Data Engineering: PySpark, Spark SQL, Python, SQL, ETL/ELT modernization.

• Agentic AI: AI Agents for ingestion, mapping, schema evolution, data quality, pipeline optimization, RCA, governance, and self-healing.

• GenAI: Azure OpenAI, RAG, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP, Vector DBs.

• AI-DLC: AI-assisted requirements, data modeling, code/pipeline generation, testing, code review, documentation, deployment, and monitoring.

• Governance: Responsible AI, guardrails, security, auditability, observability, cost monitoring, explainability, and HITL.

• DevOps/MLOps: Azure DevOps, GitHub Actions, CI/CD, MLOps, LLMOps, AI evaluation/testing.

• Leadership: Enterprise architecture, reusable AI agents/accelerators, mentoring teams, and CXO-level AI strategy/ROI discussions.

• Certifications: Databricks, Azure Solution Architect/Data Engineer, Azure OpenAI, GenAI/Agentic AI certifications are preferred.

• Education: BE/ME/BTech/MTech/BSc/MSc or equivalent engineering/science degree.

• Key success expectation: Drive 30–50% improvement in Data Engineering productivity through Agentic AI.