AI Deployment Engineer, Agent Enablement

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• Run hands-on evaluations, dogfood, launch-readiness reviews, staged rollouts, and post-launch investigations; turn findings into concrete fixes rather than one-off workarounds.
• Contribute targeted improvements to ChatGPT, Codex, and the Agent Enablement platform, including identity protocols, APIs, SDKs, docs, examples, internal tooling, partner-debugging workflows, launch guardrails, and user-facing consent or control experiences.
• Work closely with product, engineering, design, partnerships, legal, policy, security, support, and go-to-market teams to make partner launches smooth, safe, and repeatable.
• Bring structured signals from partners back to product and engineering, and turn recurring integration patterns into platform requirements, reference architectures, playbooks, and developer guidance.

Requirements

• 4–6 years of professional software engineering or AI engineering experience and are strong enough technically to contribute to the platform itself while still enjoying hands-on coding.
• Operated in a customer or partner facing roles in scoping projects, building MVPs, presenting trade-offs in solution design and architecture, understanding user value drivers as well as scoping and executing on tight implementation plans.
• Built and operated production full-stack products, APIs, backend services, developer platforms, connectors, or integrations, and can 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.
• Can work comfortably with external engineers, product leaders, and executives, and translate partner feedback into crisp technical plans and product improvements.
• Comfortable with ambiguity and rapidly changing conditions, and can turn high-level ideas into runnable prototypes, API contracts, technical specs.
• Balance urgency with judgment and can keep complex partner launches moving while staying precise about the details that matter.

Nice-to-Haves

• 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 calls, navigating ambiguity, and helping external teams ship production software.
• Strong familiarity with AI products, LLM APIs, ChatGPT or Codex surfaces, coding harnesses, developer platforms, agent workflows, or ecosystem products.
• Experience with evaluations, staged rollouts, observability, incident response, launch operations, and turning production learnings into durable platform improvements.