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Enterprise Palantir Solutions Architect

Job in Grand Rapids, Kent County, Michigan, 49528, USA
Listing for: X2 logics
Full Time position
Listed on 2026-06-14
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Cloud Computing, Data Engineering
Salary/Wage Range or Industry Benchmark: 125000 - 150000 USD Yearly USD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

Responsibilities

  • Serve as Lear’s enterprise authority for Palantir Foundry and AIP, including Ontology, Object Types, Transformations, Feature Sets, AIP Agents, Solution Designer, and developer tooling.
  • Stay current on Palantir platform releases, including AI FDE, AI Pilot, AIP enhancements, compute changes, and emerging Foundry capabilities.
  • Design scalable, well‑patterned data and AI solutions that leverage the strengths of Foundry and AIP.
  • Lead the end‑to‑end architecture of solutions — from data ingestion and harmonization to AI/analytics workflows, application experiences, and data delivery endpoints.
  • Provide architectural guidance on internal and external integrations, including APIs, event‑driven pipelines, data exchanges, partner integrations, and secure data products.
  • Facilitate architecture reviews, solution documentation, and alignment with Lear’s enterprise architecture principles.
Security, Identity, and Access Controls
  • Architect secure‑by‑design solutions using Foundry’s granular permissioning, object‑level controls, and lineage‑based access patterns.
  • Ensure compliance with enterprise security standards, regulatory needs, and global data protection policies.
Performance, Compute, and Cost Optimization
  • Optimize Foundry compute workloads (PySpark, Code Repositories, Pipelines, applications) for performance, elasticity, and cost efficiency.
  • Architect scalable infrastructure patterns for AIP agents, LLM‑backed workflows, and compute‑heavy workloads.
Data Quality, Reliability & Intelligent Monitoring
  • Ensure solutions include data quality monitoring, validation rules, schema controls, and reliability SLAs.
  • Design automated or “intelligent” data health monitoring patterns, alerting, lineage tracking, and remediation workflows.
  • Collaborate closely with ontology and data engineering teams to ensure semantic consistency and trustworthy data foundations.
Production Readiness & Operationalization
  • Conduct detailed production readiness assessments covering architecture, security, performance, data quality, observability, DR/BCP, and support models.
  • Develop reusable components, patterns, accelerators, and templates that increase solution velocity and consistency for all teams.
Platform Evangelism & Technical Leadership
  • Coach teams on Palantir architecture patterns, platform capabilities, performance optimization, and secure workflows.
  • Collaborate cross‑functionally with engineering, data science, manufacturing, quality, finance, and commercial stakeholders.
  • Contribute to platform roadmap discussions and strategic planning as Lear expands its enterprise data and AI ecosystem.
Qualifications
  • Bachelor’s in Computer Science, Information Systems, Data Engineering, or related technical field.
  • 5+ years of experience in IT architecture, data platforms, or enterprise solution design.
  • 3–5+ years of hands‑on Palantir Foundry architecture experience, including Ontology, pipelines, transformations, application development, and data modeling.
  • Strong working knowledge of Palantir AIP, including agent frameworks, LLM workflows, grounding strategies, and secure prompt design.
  • Deep understanding of identity, access management, security patterns, logging, and compliance considerations.
  • Expertise in documenting and communicating architecture designs and reviewing solutions for alignment and quality.
Preferred Qualifications
  • Experience with manufacturing, industrial systems, or supply chain operations (ISA‑95, plant hierarchies, OEE, BOM, routings, genealogy).
  • Knowledge of MLOps, LLM/RAG patterns, or operational AI systems.
  • Experience designing intelligent monitoring, observability, and reliability engineering patterns.
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