AI Deployment Engineer, Cyber

OpenAI OpenAI · AI Frontier · Tokyo, Japan · Go To Market

AI Deployment Engineer focused on helping customers safely and effectively deploy OpenAI technologies, specifically Codex and agentic workflows, for cybersecurity use cases. The role involves acting as a technical advisor, designing architectures, building prototypes, and advising on safe implementation patterns.

What you'd actually do

  1. Deeply embed with strategic customers as the technical lead for AI-enabled cybersecurity workflows, serving as a trusted partner to both security executives and technical practitioners.
  2. Lead discovery across AppSec, DevSecOps, vulnerability management, SOC/IR, detection engineering, red team, cloud security, identity, and GRC automation use cases.
  3. Build and deliver customer-facing demos, prototypes, workshops, proofs of concept, and reference architectures using OpenAI APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, and common security tools.
  4. Scope pilots with clear success criteria, data requirements, workflow integrations, evaluation methods, security constraints, safety boundaries, and human approval points.
  5. Advise customers on safe implementation patterns, including tool and function calling, structured outputs, retrieval, sandboxing, data handling, guardrails, telemetry, auditability, and approval-gated side effects.

Skills

Required

  • 5+ years of technical consulting, solutions engineering, security architecture, cyber advisory, deployment engineering, professional services, or equivalent customer-facing technical experience.
  • Strong cybersecurity domain expertise across one or more areas such as application security, cloud security, identity, vulnerability management, secure SDLC, incident response, detection engineering, threat intelligence, red teaming, or security architecture.
  • Ability to communicate credibly with CISOs, CTOs, security executives, engineering leaders, and highly technical security practitioners.
  • Hands-on experience building prototypes or production systems with APIs, Python or JavaScript, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, or other common security tooling.
  • Understanding of how to design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, sandboxing, and human-in-the-loop review.
  • Comfortable scoping pilots from ambiguous customer pain, including success metrics, required data, workflow integrations, evaluation criteria, deployment assumptions, and decision gates.
  • Evidence-first security judgment: validate findings, separate true positives from noise, document assumptions, and avoid overstating model or security claims.
  • Own problems end-to-end, operate with high throughput across multiple concurrent customer projects, and know when to stay hands-on versus create reusable leverage for the broader field.

Nice to have

  • humble attitude
  • eagerness to help colleagues
  • desire to do whatever it takes to make the team and our customers successful.

What the JD emphasized

  • partner with customers
  • technical advisors
  • hands-on solution design
  • customer-facing technical role
  • hands-on solution design
  • customer-facing technical experience
  • hands-on experience building prototypes or production systems
  • comfortable scoping pilots from ambiguous customer pain

Other signals

  • customer-facing technical role
  • deploying AI technologies in production
  • AI-enabled cybersecurity workflows