AIP Innovation Engineer - iDEA by Lear
Hybrid · Southfield, Michigan, United States
Job Summary
AIP Innovation Engineer – iDEA by Lear at Lear’s Southfield HQ (HYBRID). You’ll design, implement, and operationalize AI/LLM agent workflows within Palantir Foundry/AIP, build end-to-end data and automation solutions, and collaborate with ontology leads to shape reusable data objects. Own data ingestion, semantic grounding, and productionizing agent-based solutions, with emphasis on monitoring, guardrails, and scalable AI deployment. You’ll work with product owners and plant operations to scope high-value use cases, ship MVPs quickly, and measure success through data health, tool reuse, and performance improvements. Preferred background includes Palantir Foundry/AIP experience or adjacent stacks, plus hands-on experience with LangChain/LangGraph/CrewAI, MLOps pipelines, and cloud services (Azure/AWS). You’ll operate in a hybrid role located in Southfield, MI, contributing to enterprise AI initiatives across manufacturing contexts, with a focus on end-to-end delivery, reliability, and governance.
Required Qualifications
- 4+ years building production data/AI solutions (startup or enterprise) with hands-on ownership from ingestion to deployment
- Strong experience with LLM/agentic systems: prompt design, tool/function calling, retrieval/grounding, safety policies, and evaluation
- Proficiency with at least two of: Python, TypeScript/JavaScript, PySpark; comfort with APIs, microservices, and event driven patterns
- Experience with Palantir Foundry and/or AIP (Ontology, pipelines, transformations, apps, agents) or deep experience with adjacent stacks (e.g., LangChain/LangGraph/CrewAI/AutoGen/Semantic Kernel; vector DBs; cloud AI services) and ramp to Palantir
- Practical Data Quality & Observability experience (contracts, schema checks, lineage, alerts, evals) and operational-excellence bias
- Ability to operate without a mature EDW; wrangle messy data, define interim schemas, and harden pipelines
- Preferred: experience integrating AI into manufacturing/industrial contexts; LLMOps/MLOps; cloud experience (Azure/AWS); secure-by-design patterns
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