AIP INNOVATION ENGINEER - iDEA by LearSOUTHFIELD, MI WORLD HQ - (HYRBID)About Lear and IDEA by LearLear 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.
Position OverviewThe
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.
Key ResponsibilitiesAgentic AI & AIP Enablement- 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.
Data & Integration Engineering- 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.
Ontology Driven 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.
Reliability, Data Health & Guardrails- 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.
Productionization & Performance- 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.
Solution Delivery & Stakeholder Collaboration- 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.
Required Qualifications- 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.
Preferred Qualifications- 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.
What You'll Do in Your First 90 Days- Ship 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 Success- Time 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 Different- It'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 multi-year 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 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).
Location Code: 0090
