Leads end-to-end customer deployments, builds and manages a field engineering organization, develops delivery tools and playbooks, and reviews production code across frontend and backend systems.
About this role
This customer-facing technical role sits within an AI Deployment Engineering team that helps developers and enterprises move OpenAI technologies from prototypes into durable production use. The role focuses on cybersecurity, partnering with CISOs, security leaders, SOC teams, and practitioners to identify practical applications for AI in security-critical workflows.
The engineer leads discovery, designs reference architectures, and builds demos, prototypes, workshops, proofs of concept, and pilots. The work may cover secure code review, vulnerability triage, threat modeling, incident response, detection engineering, cloud security, GRC automation, and security validation. Named tools and methods include OpenAI APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, scanners, retrieval, structured outputs, sandboxing, guardrails, telemetry, and auditability. Candidates should have at least five years of customer-facing technical experience in areas such as technical consulting, solutions engineering, security architecture, cyber advisory, deployment engineering, or professional services, plus strong cybersecurity expertise in one or more relevant domains.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong. Always confirm everything on the official posting before applying.
Similar jobs
Leads an Applied AI Architect team supporting strategic customers in Japan, guiding AI initiatives from exploration to production and overseeing technical strategy, deployment, and team development.
Troubleshoots API failures, integrations, authentication errors, and production incidents; reproduces issues, guides customers, and automates support workflows with Python and AI tools.
Leads technical collaborations and research on hybrid quantum-classical applications for AI, high-performance computing, simulations, and digital twins.
Lead platform engineering for AI-native software development and operations, building automation, quality, monitoring, and secure systems for sensitive medical data.
Design, develop, and operate AI agent infrastructure using LLM platforms and agent frameworks to enhance business decision-making.