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AI Engineer

[OWD] Lead Data & AI Engineer (Palantir Foundry/AIP) - Canada

On-siteSeniorAI Engineerposted 3d ago
✦Role summaryAI-generated

The Lead Data & AI Engineer will design and manage Palantir Foundry pipelines, ontology components, and LLM‑powered workflows for client solutions. They will also act as the technical lead, guiding architecture, reviewing code, and ensuring production deployment in Ottawa.

Skills required

About this role

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
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