Partner AI Deployment Engineer - Aws

OpenAI OpenAI · AI Frontier · San Francisco, CA · Go To Market

This role focuses on enabling the safe and effective deployment of Generative AI applications for developers and enterprises by serving as a technical advisor to AWS field leadership and partners. The engineer will drive joint customer success, shape strategy, define engagement models, and build repeatable systems to scale adoption of OpenAI-powered solutions. Responsibilities include leading technical strategy for complex engagements, guiding customers from ideation to production, designing end-to-end AI architectures, building prototypes, establishing best practices for scalable GenAI systems, and enabling AWS and partners through workshops and playbooks. The role requires strong technical leadership, systems thinking, and the ability to balance hands-on building with strategic influence.

What you'd actually do

  1. Serve as the senior technical counterpart to AWS field leadership, building trust and credibility across regions and teams.
  2. Lead technical strategy for large, ambiguous, and high-stakes enterprise engagements.
  3. Design and communicate end-to-end AI architectures leveraging OpenAI and AWS services.
  4. Enable AWS and partners through scalable technical motions (workshops, playbooks, reference architectures, demos).
  5. Partner closely with Alliances, Product, Engineering, GTM, and Enablement to align on strategy and execution.

Skills

Required

  • 8+ years of technical consulting experience
  • managing C-level technical and business relationships with complex global organizations
  • technical leadership
  • systems thinking
  • hands-on building
  • strategic influence
  • design and communicate end-to-end AI architectures
  • build and guide development of prototypes, POCs, and reference implementations
  • establish best practices for scalable, secure, and production-ready GenAI systems
  • enable AWS and partners through scalable technical motions

Nice to have

  • ecosystem leadership
  • strong judgment

What the JD emphasized

  • scale adoption
  • production-scale AI systems
  • scale across AWS globally
  • scale
  • production deployment
  • scalable
  • partner-led delivery
  • Scale impact
  • partner success at scale

Other signals

  • driving joint customer success
  • enabling AWS and partner ecosystems to scale adoption
  • shaping strategy, defining engagement models, and building repeatable systems
  • guiding complex enterprise customers from ideation to production
  • enabling AWS and partners to independently drive deployments