Palantir Forward Deployed Engineer - Gps

This role focuses on building and deploying GenAI-enabled solutions, agentic platforms, and workflows within enterprise AI platforms for clients. The engineer will work closely with clients to identify needs, prototype, and deliver working AI solutions, applying architecture decisions for quality, safety, latency, and cost. Responsibilities include developing scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls, as well as contributing to production-quality code with strong practices in testing, CI/CD, and documentation.

What you'd actually do

  1. Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.
  2. Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  3. Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  4. Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  5. Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Skills

Required

  • software engineering
  • data engineering
  • data science
  • analytics engineering
  • GenAI/LLM-powered solutions
  • Palantir Foundry/AIP/Maven
  • leading project workstreams
  • translating business problems into AI solutions
  • building reliable, maintainable, and well-documented code

Nice to have

  • cloud environments (AWS, Azure, and/or Google Cloud)
  • common platform services
  • working directly alongside client technical teams
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling
  • ML/data science background feature engineering, experimentation or model evaluation
  • MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
  • integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
  • operating within hybrid onshore/offshore teams
  • security, privacy, and compliance considerations

What the JD emphasized

  • hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • building reliable, maintainable, and well-documented code

Other signals

  • GenAI-enabled solutions
  • agentic platforms
  • workflows across enterprise AI platforms
  • production-quality code