Staff Fullstack Engineer, Agentic Applications

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/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 have

• 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.