Lead Data & AI Engineer
Company Description
About the role We're putting together a senior team for a global consulting client building and deploying enterprise AI solutions at a fast pace. You'll take business problems from idea to production on Palantir Foundry and AIP, combining hands-on development with technical leadership. You'll guide solution design, work directly with client stakeholders, and help other engineers become 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 - Serve as the team’s Palantir lead, remaining hands-on while guiding solution design, reviewing code, coaching engineers, and resolving technical blockers - Own technical delivery of assigned solutions, translating business requirements into clear implementation plans and guiding work through production deployment - Lead technical discussions with client stakeholders, explaining design decisions, trade-offs, dependencies, and delivery risks
Qualifications
What we're looking for - Hands-on Palantir Foundry and/or AIP experience on real client or production projects - Experience developing agentic or GenAI applications is strongly preferred - 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 - Experience building reusable agent skills, tools, or components that support multiple use cases