AI Deployment Engineer, Cyber

About the team The AI Deployment Engineering team is responsible for helping developers and enterprises safely and effectively deploy OpenAI technologies in production. We act as trusted technical advisors and thought partners for customers, working side by side with their teams to identify high-value use cases, design practical architectures, and move from prototype to durable deployment. Cybersecurity is one of the most urgent domains where AI can help. Security teams are under pressure to reason across code, logs, infrastructure, tickets, alerts, and vulnerability data faster than ever. As frontier models become more capable, organizations need deep technical guidance on how to evaluate, validate, and safely deploy AI systems in security-critical workflows. About the role We are looking for a Cyber AI Deployment Engineer to partner with customers and help them apply OpenAI models, APIs, Codex, and agentic workflows to real cybersecurity use cases. You will work with CISOs, security executives, application security leaders, SOC teams, security engineering teams, and hands-on practitioners to identify where AI can create measurable security outcomes. This is a customer-facing technical role for someone who can move fluidly between executive strategy, practitioner-level cyber depth, and hands-on solution design. You will help customers evaluate and deploy workflows such as secure code review, vulnerability triage, threat modeling, remediation, SOC and incident response workflows, detection engineering, cloud security, GRC automation, and security validation. You will collaborate closely with Sales, Solutions Engineering, Product, Engineering, Research, and Security to turn customer needs into safe deployment patterns, reusable field assets, and product feedback. This role is based in our San Francisco HQ. We offer relocation support to new employees. In this role, you will: - 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. - Lead discovery across AppSec, DevSecOps, vulnerability management, SOC/IR, detection engineering, red team, cloud security, identity, and GRC automation use cases. - 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. - Scope pilots with clear success criteria, data requirements, workflow integrations, evaluation methods, security constraints, safety boundaries, and human approval points. - 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. - Translate between CISO-level outcomes and practitioner-level implementation details so each audience understands value, risk, and practical next steps. - Create reusable field assets such as demo narratives, playbooks, FAQs, objection handling, qualification guides, assessment templates, and competitive positioning. - Validate, synthesize, and deliver high-signal feedback to Product, Engineering, Research, Security, and GTM teams based on recurring customer requirements, blockers, product gaps, and emerging cyber workflows. You'll thrive in this role if you: - Have 5+ years of technical consulting, solutions engineering, security architecture, cyber advisory, deployment engineering, professional services, or equivalent customer-facing technical experience. - Bring 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. - Can communicate credibly with CISOs, CTOs, security executives, engineering leaders, and highly technical security practitioners. - Have 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. - Understand how to design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, sandboxing, and human-in-the-loop review. - Are comfortable scoping pilots from ambiguous customer pain, including success metrics, required data, workflow integrations, evaluation criteria, deployment assumptions, and decision gates. - Have evidence-first security judgment: you 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 broade

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 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 designing AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, sandboxing, and human-in-the-loop review. Comfort 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. Ability to 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 team.