Job Description
Databricks is transforming how it builds and operates People Technology — moving from traditional SaaS configuration toward an AI-native, agentic stack. You'll be the technical anchor of the People Tech pod, driving the architectural shift from workflow automation to autonomous, multi-agent systems that power HR, recruiting, workforce analytics, and employee experience at scale. This is a rare opportunity to reimagine a critical enterprise domain from the ground up using the very data and AI platform Databricks sells to the world. What you'll do Architect and build agentic systems that automate and augment People Tech workflows — onboarding, offboarding, comp analysis, policy Q&A, HR service delivery — using LLM orchestration frameworks (LangGraph, AutoGen, or equivalent). Define the agentic platform strategy for the pod: agent design patterns, tool-calling conventions, retrieval-augmented pipelines, evaluation frameworks, and human-in-the-loop guardrails. Integrate People Tech systems (Workday, Greenhouse, ADP etc.) as agent-accessible tools and data sources via Databricks Unity Catalog and MCP-style interfaces. Set the technical bar for the pod — reviewing designs, establishing engineering standards, and leading architectural reviews across the People Tech roadmap. Influence peers and stakeholders: translate agentic capability into business outcomes for People, Legal, and Finance partners, and mentor engineers in the pod on AI-first thinking. What we're looking for 8+ years of software engineering experience, with at least 2 years building production LLM or agentic applications (agents, RAG pipelines, tool-use, multi-agent orchestration). Deep fluency in Python and experience with agentic frameworks — LangChain/LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar. Strong command of enterprise integration patterns: REST/Gr
