AI Deployment Engineer, Agent Enablement

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

AI Deployment Engineer focused on enabling strategic partners to integrate OpenAI's agent-enabled tech stack, including identity, permissions, and agent-auth primitives, into their services. The role involves hands-on product engineering, technical leadership, and collaboration across internal and external teams to ensure secure, production-ready integrations and seamless user experiences.

What you'd actually do

  1. Own the technical partner journey for priority agent enablement integrations—from use-case selection and readiness assessment through architecture, prototype, implementation, evaluation, launch, rollout, and ongoing maintenance.
  2. Help partners choose and implement the right integration path across browser sign-in, connector-initiated OAuth, and agentic account linking or provisioning, with clear user journeys and safe fallback behavior.
  3. Write production and sample code, build reference implementations and test harnesses, and create the technical guidance, integration checklists, evaluations, and debugging tools that move partners from concept to production.
  4. Debug identity and agent workflows end to end: user interface state, browser redirects, PKCE/OIDC transactions, token exchange and validation, account mapping, connector callbacks, CLI or MCP handoffs, latency, retries, rate limits, logs, traces, and metrics.
  5. Review partner architectures and implementation plans for API contracts, scopes and permissions, consent, terms acceptance, and account policy, secret handling, data boundaries, privacy, reliability, and long-term maintainability.

Skills

Required

  • 4–6 years of professional software engineering or AI engineering experience
  • Strong technical skills for platform contribution and hands-on coding
  • Experience in customer or partner facing roles (scoping projects, building MVPs, presenting trade-offs)
  • Experience building and operating production full-stack products, APIs, backend services, developer platforms, connectors, or integrations
  • Ability to reason across frontend, backend, auth, data models, reliability, privacy, and UX constraints
  • Strong understanding of how plugins and connectors work with coding agents, integrating MCP servers, using CLIs and APIs to access external tools
  • Comfort working with external engineers, product leaders, and executives
  • Ability to translate partner feedback into crisp technical plans and product improvements
  • Comfort with ambiguity and rapidly changing conditions
  • Ability to turn high-level ideas into runnable prototypes, API contracts, technical specs
  • Balance urgency with judgment
  • Ability to keep complex partner launches moving while staying precise about details

Nice to have

  • High-level understanding of the identity stack, including how OAuth works
  • Experience designing or operating APIs, webhooks, schemas, SDKs, CLIs, MCP servers, tool-calling systems, or other developer-facing integrations
  • Prior customer-facing or partner-facing engineering experience, including leading technical engagements

What the JD emphasized

  • partner-facing product engineering role
  • sophisticated technical engagements
  • secure, production-ready integrations
  • agent-workflow requirements
  • identity and agent-workflow
  • production full-stack products
  • API contracts
  • scopes and permissions
  • consent
  • terms acceptance
  • account policy
  • secret handling
  • data boundaries
  • privacy
  • reliability
  • long-term maintainability
  • identity stack
  • OAuth
  • APIs
  • webhooks
  • schemas
  • SDKs
  • CLIs
  • MCP servers
  • tool-calling systems
  • developer-facing integrations
  • customer-facing or partner-facing engineering experience

Other signals

  • agent enablement
  • partner integrations
  • identity and authentication
  • developer experience