AI Infrastructure Engineer

We are hiring a Senior AI Engineer to work with a leading global automotive OEM, focused on building AI-powered tooling and infrastructure across the software development lifecycle. The role sits at the intersection of generative AI, developer tooling, SDLC automation, and scalable AI infrastructure, with automotive experience considered a strong advantage. The main focus is designing and deploying AI systems that automate the end-to-end engineering pipeline — from design through to validation — including the development of MCP (Model Context Protocol) servers and AI infrastructure that scales in production environments.Engagement detailLocation: San Francisco CaliforniaContract type: Permanent/ContractStart date: ASAPWork model: HybridBenefits: Competitive package; high-impact, high-visibility role within a major software-defined vehicle programmeRole focusDesign and build AI-powered tooling integrated across the full SDLC ecosystemArchitect end-to-end automation of the engineering pipeline, from design and development through to testing and validation, using generative AIBuild, deploy, and maintain MCP (Model Context Protocol) servers as part of a scalable AI infrastructure strategyDevelop AI systems that perform reliably and scale in production environmentsCollaborate with engineering, product, and infrastructure teams to embed AI capabilities into existing workflows and toolingDefine and document AI system architectures, data flows, and integration patternsEvaluate emerging AI tooling and frameworks and provide recommendations to technical leadershipSupport the rollout and adoption of AI tooling across engineering teamsRequirements5–6 years of hands-on experience building production-grade Generative AI systemsStrong experience designing and building AI tooling and infrastructure — not just model usage, but the systems, pipelines, and infrastructure around themProven experience with MCP servers and agentic AI architecturesTrack record of delivering scalable AI systems in complex, real-world engineering environmentsDeep understanding of SDLC processes and practical experience automating them with AIProficiency in one or more programming languages commonly used in AI/ML engineering contextsExperience integrating AI tooling into CI/CD pipelines and developer workflowsNice to haveExperience in the automotive industry or adjacent regulated or complex engineering domainsFamiliarity with E2E automated testing pipelines and AI-assisted validation frameworksExperience with embedded software environments or vehicle software platformsBackground working with multi-domain or cross-functional engineering teamsNext StepsPress "Apply"We will review your applicationIf qualified, you will be accepted into the network and can be considered for this and similar positions