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Lear

Supplies automotive seating and electronic systems.

AIP Innovation Engineer - Idea by Lear

AI Application Engineering +1Updated on Nov 14
Full-timeSouthfield, MI, USANo salary listed

About the Job

Requirements

  • At least 4 years of experience building production data or AI solutions, with hands-on ownership from ingestion through deployment.
  • Strong experience with large language model and agentic systems, including prompt design, tool or function calling, retrieval and grounding, safety policies, and evaluation.
  • Proficiency in at least two of Python, TypeScript or JavaScript, and PySpark; familiarity with APIs, microservices, and event-driven patterns.
  • Experience with Palantir Foundry and/or AIP, including ontology, pipelines, transformations, applications, or agents; alternatively, deep experience with adjacent stacks and the ability to ramp up to Palantir quickly.
  • Practical data quality and observability experience, including contracts, schema checks, lineage, alerts, and evaluations.
  • Ability to work in an environment without a mature enterprise data warehouse, wrangle messy data, define interim schemas, and harden pipelines.
  • Must be legally authorized to work in the United States without sponsorship.

Responsibilities

  • Design and implement AIP agents and large language model-backed workflows, including prompt flows, tools, skills, and policies grounded in Foundry ontology objects and feature sets.
  • Leverage Palantir platform innovations such as AI FDE and AI Pilot, bringing forward relevant capabilities.
  • Identify opportunities through hands-on data work and analysis for harmonization, transformations, and semantic grounding to support AI and large language model solutions.
  • Identify internal and external integrations, including partner data, supplier feeds, and SaaS applications, using appropriate security, throttling, and resilience patterns.
  • Partner with ontology leaders to propose and refine ontology objects, relationships, and reusable semantics that enable automation and cross-use-case reuse.
  • Influence data shaping for retrieval-augmented generation and grounding, action execution, reasoning chains, and multi-agent handoffs.
  • Collaborate with the Data Quality Lead on data quality checks, freshness, schema drift, lineage, latency service-level objectives, large language model output evaluations, red-team prompts, and safety guardrails.
  • Establish offline and online evaluation harnesses for agent workflows and prompts, and track regression metrics and cost and performance key performance indicators.
  • Build continuous integration and continuous delivery pipelines, infrastructure as code where applicable, and observability using logs, traces, and metrics for AIP agents and Foundry applications.
  • Work with product owners, plant operations, quality, supply chain, and finance to scope high-value use cases, deliver minimum viable products, and iterate them to scale.
  • Provide technical documentation, runbooks, and handoffs to operations teams.
  • In the first 90 days, ship one or two targeted AIP agent minimum viable products grounded in existing ontology objects and iterate based on evaluation feedback.
  • Build or harden ingestion-to-delivery paths for a high-value use case, including intelligent data health monitoring and cost and performance dashboards.
  • Partner with ontology leads to propose reusable object patterns that enable at least two additional AI use cases.

Desired Qualifications

  • Prior experience integrating AI into manufacturing or industrial contexts, such as mapping to ISA-95 hierarchies, overall equipment effectiveness, quality or nonconformance, routings, and genealogy.
  • Large language model operations or machine learning operations experience, including MLflow, model registries, evaluation pipelines, and continuous integration and delivery for prompts or agents.
  • Cloud experience with Azure or AWS for scaling inference, storage, and data movement.
  • Familiarity with secure-by-design patterns, including identity, access, secrets, personally identifiable information handling, and audit logging.
  • Experience building agentic AI systems with LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
  • Experience implementing retrieval-augmented generation with hybrid retrieval, adaptive chunking, and domain-specific guardrails.
  • Experience designing distributed fine-tuning, including QLoRA or instruction tuning, and establishing large language model operations or machine learning operations pipelines using MLflow, Kubernetes, SageMaker, or Ray.
  • Experience delivering document intelligence, including multimodal parsing, extraction, and validation, and operational AI such as recommendation, anomaly detection, and forecasting.

About the Company

Lear

Supplies automotive seating and electronic systems.

Lear Corporation provides automotive seating and electronic systems. It designs, manufactures, and supplies high-quality seating options and integrated E-Systems software to automotive manufacturers worldwide. How it works: its seating solutions include advanced ergonomics and premium materials (Leather from Eagle Ottawa, textiles from Guilford), while E-Systems involve software that enhances vehicle functionality. The company operates through a global manufacturing network across 39 countries, emphasizing operational excellence, sustainability, and scalable supply. How it differs: it combines seating expertise with electronic systems, supported by a global footprint, strong supplier relationships, and a focus on sustainable practices. Its goal: to redefine mobility by delivering reliable, comfortable, and connected in-vehicle experiences through sustainable technology.

Company size
10,001+
Company stage
IPO
Headquarters
Southfield, MI
Founded
1917

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