Manager, Partner AI Deployment Engineering - Aws

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

Manager for AI Deployment Engineering focused on AWS partnerships, responsible for leading a team that enables partners to build, deploy, and operationalize AI applications on OpenAI's platform. The role involves guiding customers through the full AI implementation lifecycle, collaborating with internal and external stakeholders, and driving production deployments and API adoption.

What you'd actually do

  1. Lead, mentor, and grow a team of AI Deployment Engineers supporting strategic AWS partner engagements and customer deployments.
  2. Define the operating model, engagement strategy, and technical priorities for the AWS Partner ADE pod.
  3. Partner closely with AWS partner leadership, solution architects, delivery organizations, and customer stakeholders to accelerate production adoption of OpenAI technologies.
  4. Guide teams through complex generative AI and traditional ML deployments, including architecture reviews, implementation planning, security considerations, evaluation strategies, and operational readiness.
  5. Serve as a senior technical escalation point for critical partner and customer engagements, helping teams navigate ambiguity and drive successful outcomes.

Skills

Required

  • 8+ years of experience in technical customer-facing roles, including managing executive-level technical and business relationships with enterprise organizations and strategic partners.
  • 3+ years of experience leading high-performing technical teams in solutions engineering, deployment engineering, forward-deployed engineering, customer engineering, or post-sales environments.
  • Hands-on experience deploying Generative AI and traditional ML systems into production environments, including familiarity with LLM application architectures, evaluation methodologies, orchestration frameworks, and operational best practices.
  • Strong knowledge of AWS cloud infrastructure and modern cloud-native architectures, including networking, security, compute, storage, observability, and application deployment patterns.
  • Experience working with cloud ecosystem partners, systems integrators, consultancies, or technical alliance organizations.
  • Technical depth in software engineering or solution development using languages such as Python, JavaScript, or TypeScript.
  • Comfort balancing strategic leadership with hands-on technical engagement and operational execution.
  • Effective communicator who can translate complex technical concepts into clear business outcomes for executives, partners, developers, and customers alike.
  • Strong sense of ownership, humility, and curiosity, with a willingness to learn quickly and help others succeed in ambiguous, fast-moving environments.

What the JD emphasized

  • production adoption
  • production deployments
  • operational readiness
  • enterprise AI deployment
  • cloud-native AI architectures
  • responsible AI adoption

Other signals

  • driving them into production
  • scale successful deployments
  • technical success strategy
  • scale repeatable deployment motions
  • full AI implementation lifecycle
  • production deployment, optimization, and adoption growth
  • production deployments
  • API adoption growth
  • accelerate production adoption
  • complex generative AI and traditional ML deployments
  • operational readiness
  • senior technical escalation point
  • platform improvements, tooling enhancements, and deployment best practices
  • scalable enablement frameworks, reference architectures, and repeatable deployment patterns
  • reduce time-to-production
  • operational excellence
  • enterprise AI deployment
  • cloud-native AI architectures
  • responsible AI adoption