AI Deployment Engineer | Codex

OpenAI OpenAI · AI Frontier · Munich, Germany · Go To Market

This role focuses on helping enterprise customers adopt OpenAI's coding tools (Codex) by acting as a technical partner. The engineer will design, validate, and scale AI workflows, build demos and integrations, and provide technical enablement and content. The role involves close collaboration with sales, product, and applied engineering teams to gather customer feedback and influence product direction.

What you'd actually do

  1. Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows.
  2. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout.
  3. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process.
  4. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely.
  5. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex.

Skills

Required

  • 5+ years of technical consulting, post-sales engineering, solutions architecture, or similar experience working directly with customers
  • Active power user of AI coding tools
  • Experience delivering large, high-impact workshops or technical training to engineering teams
  • Experience contributing technical guides, patterns, or examples publicly
  • Ability to communicate complex technical concepts clearly and persuasively
  • Experience working in ambiguous, rapidly evolving problem spaces
  • Customer success, reliability, safety, and operational excellence

Nice to have

  • Deeply customized own developer workflow
  • Have a point of view on what makes engineers more productive
  • Craft engaging, hands-on, and outcomes-driven sessions
  • Care about clarity, pedagogy, and community impact
  • Framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows
  • Solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption

What the JD emphasized

  • power user of AI coding tools
  • deeply customized your own developer workflow
  • build high-quality demos, reference implementations, and workflow automations
  • using Codex itself as part of your development process
  • technical content including examples, guides, patterns, and best practices
  • customer success, reliability, safety, and operational excellence

Other signals

  • customer adoption
  • deployment
  • AI workflows
  • developer productivity