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.

• Contract type:
  Permanent/Contract
• Benefits:
  Competitive package; high-impact, high-visibility role within a major software-defined vehicle programme

• Design and build AI-powered tooling integrated across the full SDLC ecosystem
• Architect end-to-end automation of the engineering pipeline, from design and development through to testing and validation, using generative AI
• Build, deploy, and maintain MCP (Model Context Protocol) servers as part of a scalable AI infrastructure strategy
• Develop AI systems that perform reliably and scale in production environments
• Collaborate with engineering, product, and infrastructure teams to embed AI capabilities into existing workflows and tooling
• Define and document AI system architectures, data flows, and integration patterns
• Evaluate emerging AI tooling and frameworks and provide recommendations to technical leadership
• Support the rollout and adoption of AI tooling across engineering teams

• 5–6 years of hands-on experience building production-grade Generative AI systems
• Strong experience designing and building AI tooling and infrastructure — not just model usage, but the systems, pipelines, and infrastructure around them
• Proven experience with MCP servers and agentic AI architectures
• Track record of delivering scalable AI systems in complex, real-world engineering environments
• Deep understanding of SDLC processes and practical experience automating them with AI
• Proficiency in one or more programming languages commonly used in AI/ML engineering contexts
• Experience integrating AI tooling into CI/CD pipelines and developer workflows

• Experience in the automotive industry or adjacent regulated or complex engineering domains
• Familiarity with E2E automated testing pipelines and AI-assisted validation frameworks
• Experience with embedded software environments or vehicle software platforms