Hi Professionals,
Title: Agentic AI Architect with Azure Databricks
JD:
Top 5 must-have areas
Azure Databricks / Lakehouse
Strong Data Engineering + PySpark/SQL
Agentic AI / AI Agents
GenAI, RAG, Azure OpenAI, LangGraph/MCP
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.
Agentic AI Architect with Azure Databricks1azure databricks,genai,Azure OPENAI,RAG,Agentic AI Architect,Strong Data Engineering + PySpark/SQL,LangGraph/MCPN/AC2CUnited States