Staff Fullstack Engineer, Agentic Applications

Databricks•Mountain View, CA

6d•$192,000 - $260,000

About The Position

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.

Requirements

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/GraphQL APIs, event-driven architecture, and connecting SaaS HR/HCM platforms programmatically.
Experience with data platforms — Databricks, Spark, or equivalent — and building AI applications on top of lakehouse or warehouse architectures.
Track record as a technical lead: driving architectural decisions, writing RFCs, and raising the quality bar across a team without relying on management authority.

Nice To Haves

Prior experience in People Tech, HR tech, or internal tooling domains.
Familiarity with Workday, Greenhouse or similar enterprise HR platforms — especially via API or integration layer.
Experience evaluating and red-teaming LLM agents for safety, reliability, and correctness in sensitive business contexts.

Responsibilities

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.