AI Deployment Manager (Builder) - Tokyo
Apply at source. OpenAI handles the application directly; Houtini doesn't take a fee from candidates or companies. We curate which companies appear; the listings come from yubhub.
What the team is looking for.
The AI Deployment Manager role is a specialist post-sales enablement role focused on delivering high-impact enablement and adoption services across OpenAI’s product suite.
This role is responsible for designing and delivering technical enablement experiences that support a repeatable adoption framework– driving sustained activation, expanding breadth and depth of usage, and measurable business value across OpenAI’s product suite, including ChatGPT Enterprise, Codex, Agents, and the API.
In this role, you will:
- Own the technical enablement of OpenAI products, including ChatGPT Enterprise, Codex, Agents, and API capabilities, helping define effective enablement patterns that support adoption across customer segments
- Lead customer training and enablement across the full customer lifecycle, from initial onboarding through expansion, optimization, and long-term adoption.
- Design and deliver high-impact training engagements, including onboarding sessions, advanced capability trainings, executive briefings, hackathons, and hands-on workshops for audiences ranging from senior leaders to working teams.
- Drive customer activation, sustained usage, and measurable business value through structured enablement and deployment programs designed for durable adoption at scale
- Partner closely with Sales, AI Success Engineers, Solutions Engineering, and Product teams to ensure seamless handoff from pre- to post-sale and consistent customer experience.
- Develop and refine reusable training assets, playbooks, and best practices based on patterns observed across customers and regions.
- Gather customer feedback from training and enablement engagements, synthesize themes across accounts, and relay insights to internal stakeholders to inform product and program improvements.
You’ll thrive in this role if you:
- Have 4+ years of experience in customer-facing or instructional roles, engaging C-level and senior technical audiences in complex enterprise environments.
- Possess exceptional presentation and communication skills, particularly when conveying the value of technical concepts clearly to senior and executive-level audiences.
- Strong technical depth across coding, agents, and APIs, with a practical understanding of how AI systems are built, evaluated, and operated in production, including RAG, evaluation strategies, fine-tuning, and key tradeoffs.
- Proven experience leading structured technical trainings, such as API bootcamps, workshops, or enablement sessions, with the ability to design learning journeys, handle live questions, and reason through problems in real time.
- Strong ability to connect technical features and capabilities to concrete business outcomes such as productivity, efficiency, cost reduction, risk mitigation, or revenue impact.
- Have a humble attitude, an eagerness to help others, and a desire to pick up whatever knowledge you're missing to make both your team and our customers succeed.
- Comfortable thinking on their feet in live customer settings, adapting quickly to new information, shifting priorities, and real-time questions while maintaining clarity, ownership, and momentum.
- Are personally committed to fostering the safe and ethical evolution of AI.
- technical enablement
- instructional design
- customer advisory
- presentation
- communication
- coding
- agents
- APIs
- AI systems
- evaluation strategies
- fine-tuning
Other roles you might consider.
Filtered through the same AI-companies allowlist.
Supervisor, Fiber
xAI
Associate Data Center Operations Technician
xAI
Enterprise Systems Manager, Recruiting Systems
OpenAI
Technical Community Manager, Campus Leaders
OpenAI
Sr. IT Data Engineer
xAI
CyberSecurity Engineer, Incident Response Lead
Mistral AI
New to AI work? Start with these.
Six pieces of orientation. Most AI-company job specs assume you've done this kind of hands-on work already. If you haven't, an afternoon with one of these is the cheapest way to close the gap.
Claude Desktop, from zero.
The agentic-AI assistant most of the people you'd be working alongside use every day. Install, configure, first useful prompts.
What MCPs areThe best MCPs for Claude Desktop.
MCP servers extend an AI assistant with tools and data. The catalogue most teams use. Useful technical context for any AI-engineering role.
Code with AIClaude Code, the complete beginners' guide.
The CLI for AI-paired development. Required reading if you're applying for any engineering role that mentions agents, or any role full stop.
Run a local modelHow to set up LM Studio.
Running a model on your own machine teaches you more about how AI products work in three hours than a year of using ChatGPT will.
The hardware realityBeginner's guide to AI hardware.
What the infrastructure under the model actually looks like. Useful context for infrastructure, applied-AI and hardware roles.
Browse the stackMCP catalogue.
Eleven MCP servers Houtini maintains or recommends. Each detail page describes a real piece of working AI infrastructure.