AIP Innovation Engineer - iDEA by Lear

AIP INNOVATION ENGINEER - iDEA by LearSOUTHFIELD, MI WORLD HQ - (HYBRID)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.Position OverviewThe AIP Innovation Engineer is a hands‐on builder and visionary who demonstrates "what is possible" with AIP/AI/Agentic AI across existing and new Foundry solutions. You will 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 end‐to‐end engineering role spans data ingestion, semantic grounding, agent design, apps/APIs, productionization with intelligent monitoring, observability, and guardrails baked in.Key ResponsibilitiesAgentic AI & AIP EnablementDesign and implement AIP agents and LLM‐backed workflows (prompt flows, tools, skills, policies) grounded in Foundry Ontology objects and feature sets.Leverage and stay current on Palantir's platform innovations (AI FDE, AI Pilot), bringing forward the right capabilities at the right time.Data & Integration EngineeringIdentify 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.Ontology Driven AIPartner 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.Reliability, Data Health & GuardrailsCollaborate with Data Quality Lead for intelligent monitoring: data quality checks, freshness, schema drift, lineage, latency SLOs, LLM output quality evaluations, "red team" prompts, and safety guardrails.Establish evaluation harnesses (offline/online) for agent workflows and prompts; track regression metrics and cost/performance KPIs.Productionization & PerformanceBuild CI/CD pipelines, IaC where applicable, and observability (logs, traces, metrics) for AIP agents to ensure they operate reliably at scale and can be quickly diagnosed, tuned, and improved.Solution Delivery & Stakeholder CollaborationWork 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.Required Qualifications4+ 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.Preferred QualificationsPrior work integrating AI into manufacturing/industrial contexts (e.g., mapping to ISA95 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.What You'll Do in Your First 90 DaysShip 1‐2 targeted AIP agent MVPs grounded on existing ontology objects and iterate using eval feedback.Build or harden ingestion delivery paths for a high‐value use case, including intelligent data health monitoring and simple cost/perf dashboards.Partner with Ontology Leads to propose reusable object patterns that enable at least two additional AI use cases.How We'll Measure SuccessTime to first value for new AI use cases (from scoped to MVP in weeks, not months).Reuse rate of agent tools, connectors, and ontology objects across teams.Data health SLOs met (freshness, schema stability, error budget) and measurable improvements in LLM/agent eval metrics.Production reliability (MTTR, incident count) and cost/performance improvements over baselines.Why This Role is DifferentIt's not a pure research or model‐only role—you'll build end‐to‐end systems where models, data, and software meet.You'll help shape Lear's enterprise ontology to amplify automation and speed across solutions.You'll be part of a high‐performing team with executive sponsorship and a multiyear commitment to Foundry + AIP.Nice to Have Experience (Translatable if not Palantir)Built agentic AI systems using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel.Implemented RAG with hybrid retrieval, adaptive chunking, and domainspecific guardrails.Designed distributed finetuning (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).Lear Corporation is an Equal Opportunity Employer, committed to a diverse workplace.#J-18808-Ljbffr