Lead Palantir Forward Deployed Engineer - Gps

Lead Palantir Forward Deployed Engineer responsible for architecting and delivering GenAI solutions, including LLM-enabled applications, agentic workflows, and RAG pipelines, in production environments for clients. This role involves leading engineering pods, setting technical direction, and ensuring quality through evaluation frameworks.

What you'd actually do

  1. Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
  2. Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability.
  3. Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.
  4. Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  5. Lead FDE pods of 2–5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health

Skills

Required

  • software engineering
  • data engineering
  • data science
  • analytics engineering
  • building and deploying GenAI/LLM-powered solutions
  • Palantir Foundry/AIP/Maven
  • leading project workstreams/engagements
  • translating business problems into AI solutions
  • building reliable, maintainable, and well-documented code
  • cloud environments (AWS, Azure, and/or Google Cloud)

Nice to have

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments

What the JD emphasized

  • GenAI solutions into production
  • LLM-enabled applications
  • agentic workflows
  • production-grade quality and scalability
  • evaluation frameworks
  • quality, hallucination risk, safety, latency, cost, and governance
  • engineering quality bars

Other signals

  • GenAI solutions into production
  • LLM-enabled applications
  • RAG pipeline design
  • evaluation frameworks