About the position

Lear is a global Tier 1 automotive supplier of Seating and E-Systems. Through IDEA by Lear (Innovation, Digital, Engineering & Automation), we’re executing a multiyear digital transformation powered by Palantir Foundry and Palantir AIP to unify data, accelerate automation, and scale AI driven decisioning across our business and plants worldwide. We’re building an elite team to turn this investment into impact. As an AIP Innovation Engineer, you’ll be on the front line—accelerating our AI adoption by designing and delivering AI automation solutions that plug into our Foundry ecosystem (Foundry AIP) and deliver measurable outcomes. The AIP Innovation Engineer is a hands on builder and visionary to demonstrate “what is possible” with AIP/AI/Agentic AI across existing and new Foundry solutions. You’ll design, implement, and operationalize LLM/agent workflows, integrate internal and external data sources, and partner with Ontology Leads to shape data for maximum automation. This is not a “model only” role; it’s an end to end engineering role that spans data ingestion → semantic grounding → agent design → apps/APIs → productionization—with intelligent monitoring, observability, and guardrails baked in. We prefer Palantir experience (Foundry + AIP, features like AI FDE and AI Pilot), but will consider strong hands on candidates from adjacent toolchains who can quickly ramp.

Responsibilities

• Design and implement AIP agents and LLM backed workflows (prompt flows, tools, skills, policies) that are grounded in Foundry Ontology objects and feature sets.
• Leverage and stay current on Palantir’s platform innovations (e.g., AI FDE, AI Pilot), bringing forward the right capabilities at the right time.
• Identify opportunities via hands-on data work and analysis to drive necessary harmonization/transforms, and semantic grounding to support AI/ LLM-based solutions.
• Identify internal and external integrations (partner data, supplier feeds, SaaS apps) with appropriate security, throttling, and resilience patterns to bring the right data together, securely to leverage AI.
• Partner with Ontology Leaders to propose and refine ontology objects, relationships, and reusable semantics that unlock automation and cross use case reuse.
• Influence data shaping required for RAG/grounding, action execution, reasoning chains, and multi‑agent handoffs.
• Collaborate with Data Quality Lead for intelligent monitoring: data quality checks, freshness, schema drift, lineage, latency SLOs, evals for LLM output quality, “red team” prompts, and safety guardrails.
• Establish evaluation harnesses (offline/online) for agent workflows and prompts; track regression metrics and cost/performance KPIs.
• Build CI/CD pipelines, IaC where applicable, and observability (logs, traces, metrics) for AIP agents to ensure AIP agents and Foundry applications operate reliably at scale and can be quickly diagnosed, tuned, and improved.
• Work closely with product owners, plant operations, quality, supply chain, and finance to scope high value use cases; rapidly deliver MVPs and iterate to scale.
• Provide clear technical documentation, runbooks, and handoffs to operations teams.

Requirements

• 4+ years building production data/AI solutions (startup or enterprise); demonstrated 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). If not Palantir, deep experience with adjacent stacks (e.g., 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 roll up sleeves to wrangle messy data, define interim schemas, and harden pipelines.

Nice-to-haves

• Prior work integrating AI into manufacturing/industrial contexts (e.g., mapping to 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.
• Built agentic AI systems using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel.
• Implemented RAG with hybrid retrieval, adaptive chunking, and domain‑specific guardrails.
• Designed distributed fine‑tuning (e.g., QLoRA, instruction tuning) and stood up LLMOps/MLOps pipelines (MLflow, K8s, SageMaker, Ray).
• Delivered document intelligence (multimodal parsing, extraction, validation) and operational AI (recommendation, anomaly detection, forecasting).