# Manager, AI Deployment Engineering

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA, New York, NY
**Role:** Engineering Management
**Salary:** $251k – $335k/yr
**Experience:** 8+ years
**Skills:** Ai Deployment, Machine Learning, Generative AI, Solutions Architecture, Technical Leadership, Enterprise Deployment, Cloud Infrastructure, Data Platforms, Security, Governance, Evaluation Frameworks
**Posted:** 2026-07-06

> Lead and develop a team of AI Deployment Engineers to drive technical success for OpenAI's enterprise customers in Professional Services, Media, Telco, and Private Equity. Coach on architecture, production deployment, and scaling AI systems while translating customer needs into product feedback.

## Job Description

## Responsibilities
- Build, manage, and develop a high-performing team of AI Deployment Engineers supporting enterprise customers across Professional Services, Media and Entertainment, Telecommunications, and Private Equity.
- Own the quality and impact of the team’s work across solution design, implementation, production readiness, adoption, and expansion.
- Coach the team through complex decisions involving architecture, model selection, evaluations, reliability, latency, safety, security, governance, and cost.
- Establish a clear operating model for prioritizing accounts and engagements based on customer need, strategic value, technical complexity, and the potential for repeatable impact.
- Serve as a senior technical escalation point during critical launches, production incidents, complex integrations, and high-stakes customer decisions.
- Partner with customer executives and technical leaders to connect deployment decisions to measurable business and operational outcomes.
- Help customers progress from isolated experimentation to production deployments and sustained adoption across teams, workflows, and business units.
- Work closely with Sales and Solutions Engineering to create continuity across the customer lifecycle and maintain shared accountability for customer success.
- Translate customer needs and recurring deployment challenges into actionable feedback for Product, Research, Engineering, Security, and other internal teams.
- Distinguish scalable market patterns from bespoke requests and advocate for investments that can benefit multiple customers.
- Develop reusable architectures, evaluation approaches, playbooks, tooling, and enablement that improve time to value across the enterprise portfolio.
- Create mechanisms to measure production deployments, usage, adoption, customer outcomes, delivery quality, team capacity, and the impact of reusable work.
- Hire thoughtfully, raise the technical and leadership bar of the organization, and foster a culture of accountability, curiosity, collaboration, and continuous learning.
- Represent OpenAI with credibility and sound judgment in conversations with senior customer and internal stakeholders.

## Requirements
- Significant experience managing customer-facing technical teams, such as Solutions Architects, Deployment Engineers, Forward Deployed Engineers, Technical Account Managers, or similar functions.
- Built or led teams responsible for deploying complex software, data, machine learning, or AI systems in enterprise environments.
- Maintain enough technical depth to evaluate architectures, ask incisive questions, challenge assumptions, and coach engineers through difficult implementation decisions.
- Experience taking AI, machine learning, or other technically complex systems from prototype to production.
- Understand the requirements of production systems, including reliability, observability, security, privacy, data governance, evaluation, and operational readiness.
- Led teams through ambiguity, competing priorities, escalations, and rapidly changing products or markets.
- Can translate between technical details, customer needs, product strategy, and business outcomes.
- Experience working with large, complex organizations that include multiple business units, stakeholder groups, procurement processes, or governance requirements.
- Strong executive presence and can build trust with engineering leaders, business executives, security teams, and other senior stakeholders.
- Designed operating models, coverage strategies, prioritization frameworks, or repeatable delivery processes for a growing technical organization.
- Strong cross-functional partner who can navigate disagreement directly while maintaining trust and shared accountability.
- Use data and clear principles to allocate limited resources across a large portfolio of opportunities.
- Care deeply about developing people and have a track record of coaching team members, raising performance, and building inclusive teams.
- Energized by the opportunity to help organizations adopt frontier AI responsibly and translate emerging capabilities into durable value.

## Nice-to-Haves
- Leading Solutions Architecture, AI deployment, forward-deployed engineering, professional services, technical consulting, or customer engineering teams.
- Deploying generative AI, machine learning, developer platforms, cloud infrastructure, data platforms, or other complex enterprise technologies.
- Working with customers in Professional Services, Media and Entertainment, Telecommunications, Private Equity, or similarly complex enterprise segments.
- Supporting global customers, multi-business-unit deployments, regulated workflows, or large-scale organizational transformation.
- Building technical enablement, reusable solution patterns, evaluation frameworks, or customer deployment methodologies.
- Direct experience in every listed industry is not required. We value leaders who can identify common technical and organizational patterns while adapting their approach to different customer environments.

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