Job Summary

We are seeking a technically deep, creativity-driven AI Deployment Engineer who is already a power user of AI coding tools and passionate about pushing the boundaries of developer productivity.  This is a highly cross-functional role that blends technical architecture, product strategy, and customer-facing leadership. Partner directly with engineering leaders and hands-on builders to design, validate, and scale advanced AI workflows, often using Codex to prototype and build the very demos, integrations, and automations customers ultimately adopt.

Key Responsibilities

• Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows.
• Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout.
• Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process.
• Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely.
• Contribute technical content including examples, guides, patterns, and best practices for OpenAI EBU Cookbook to help accelerate work with Codex.
• Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams.
• Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows.
• Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

Skill Requirements

• Have 5+ years of technical consulting, post-sales engineering, solutions architecture, or similar experience working directly with customers.
• Are an active power user of AI coding tools and have deeply customized your own developer workflow; you have a point of view on what makes engineers more productive.
• Enjoy building scrappy, high-signal demos, integrations, and prototypes that clearly articulate what Codex can enable, often using Codex to accelerate your own development process.
• Have experience delivering large, high-impact workshops or technical training to engineering teams and know how to craft sessions that are engaging, hands-on, and outcomes-driven.
• Have contributed technical guides, patterns, or examples publicly and care about clarity, pedagogy, and community impact.
• Communicate complex technical concepts in clear, persuasive written and verbal form especially when helping customers make strategic decisions about where and how to apply AI.
• Are excited by ambiguous, rapidly evolving problem spaces and enjoy iterating toward novel solutions hand-in-hand with customers.
• Care about customer success, reliability, safety, and operational excellence as much as you care about technical ingenuity.

Other Requirements

• Familiarity with OpenAI’s models, APIs, Codex, Codex Security, Daybreak, or related cyber capabilities.
• Experience working with security-product companies, global systems integrators, or cybersecurity partners.
• Depth across application security, cloud security, identity, secure SDLC, vulnerability management, detection and response, or attacker tradecraft.
• Experience developing repeatable field assets, technical enablement, or product feedback mechanisms across multiple regions.

Preferred Qualifications

• Master’s degree in computer science, Artificial Intelligence, Data Science, Engineering, or a related field, or relevant cloud, AI, security, architecture, or data certifications.
• Experience with Azure, AWS, or Google Cloud; enterprise data platforms; vector databases and search; API management; containers and Kubernetes; DevSecOps; and MLOps/LLMOps toolchains.
• Experience in consultative or presales solutioning, regulated-industry environments, architecture governance, and creation of reusable reference architectures, accelerators, evaluation suites, and bid-response artifacts.