[OWD] Senior Palantir Data / AI Engineer

Company Description
About the role We're putting together a senior team for a global consulting client that is building and deploying enterprise AI solutions at a fast pace. You'll work in a small delivery pod, taking business problems from idea to production on Palantir Foundry and AIP. You'll also be the person in your pod who helps other engineers get productive on the platform.

Job Description
What you'll do Build and manage Foundry data pipelines, ontology components, and applications Design semantic and context layers with Palantir's ontology: Object, Link, and Action Types, Interfaces, Property Sets Build LLM-powered workflows with AIP Logic, AIP Automate, and AIP Assist Take agentic AI use cases from prototype to production: build, test, deploy, monitor Act as the Palantir go-to within your pod by pairing, reviewing, and coaching teammates

Qualifications

What we're looking for Hands-on Palantir Foundry and/or AIP experience on real client or production projects Hands-on experience building agentic or GenAI applications, across several stages of the lifecycle (experimentation, development, testing, production, monitoring) Experience building reusable agent skills, tools, or components that support multiple use cases Strong software engineering and problem-solving skills Experience integrating AI with enterprise data sources, APIs, and business systems Solid data engineering background across structured and unstructured data Comfortable with fast delivery cycles and requirements that change Strong English and clear communication with both technical and business people Able to work EST business hours Nice to have Solutions deployed to production inside Palantir Ontology Linter, Data Lineage, CBAC, Foundry Marketplace Semantic layers, ontologies, or knowledge graphs (Azure, AWS, GCP, Palantir, or Databricks) Azure, Azure AI Foundry, Databricks / Genie CI/CD with GitHub, DevOps / DevSecOps tooling Experience deploying models from OpenAI, Anthropic, or other LLM providers, including model selection and cost optimization