About this role
Job title: AIP INNOVATION ENGINEER – iDEA by Lear
About the Role Join Lear as an AIP Innovation Engineer to accelerate AI adoption by architecting and delivering AI automation solutions within the Foundry ecosystem (Foundry AIP). This role blends hands-on engineering with strategic foresight to demonstrate what’s possible with agentic AI across existing and new Foundry solutions. You will design, implement, and operationalize AI workflows that drive measurable outcomes and scale across plants worldwide.
What You'll Do
- Design and implement AIP agents and LLM-backed workflows grounded in Foundry Ontology objects and feature sets.
- Leverage Palantir platform innovations (AI FDE, AI Pilot) to bring capabilities at the right time.
- Identify data opportunities and perform integration including internal/external data sources with security, throttling, and resilience patterns.
- Partner with Ontology Leaders to refine ontology objects, relationships, and reusable semantics to unlock automation.
- Build intelligent monitoring, guardrails, evaluation harnesses, and track KPI/cost/performance.
- Build CI/CD pipelines, IaC, and observability for AIP agents and Foundry apps to operate reliably at scale.
- Collaborate with product owners, plant operations, quality, supply chain, and finance to scope high-value use cases and rapidly deliver MVPs.
What We're Looking For
- 4+ years building production data/AI solutions with end-to-end ownership from ingestion to deployment.
- Strong experience with LLM/agentic systems: prompt design, tool calls, retrieval/grounding, safety policies, and evaluation.
- Proficiency with at least two of: Python, TypeScript/JavaScript, PySpark; comfortable with APIs, microservices, and event-driven patterns.
- Experience with Palantir Foundry and/or AIP (Ontology, pipelines, transformations, apps, agents). If not Palantir, deep experience with adjacent stacks (LangChain/LangGraph/CrewAI/AutoGen/Semantic Kernel; vector DBs; cloud AI services) and the ability to ramp to Palantir quickly.
- Practical Data Quality & Observability experience (contracts, schema checks, lineage, alerts, evals) and a bias toward operational excellence. Comfortable working without a mature EDW—able to wrangle messy data, define interim schemas, and harden pipelines.
Nice to Have
- Prior work integrating AI into manufacturing/industrial contexts (e.g., ISA-95 hierarchies, OEE, quality/NCR, routings, genealogy).
- LLMOps/MLOps experience (MLflow, model registries, eval pipelines, CI/CD for prompts/agents).
- Cloud experience (Azure/AWS) for scaling inference, storage, and data movement.
- Familiarity with secure by design patterns: identity, access, secrets, PII handling, audit logging.