Engineering Manager - Generative AI Engineering

TEAM

Software development in the automotive industry comes with unique challenges. Developers need to build, test, secure, and deploy software across cloud environments, mobile devices, embedded systems, and vehicles. They also need modern AI-enabled tools that are safe, governed, observable, and integrated with enterprise systems.
 
The Developer Productivity function builds tools, platforms, and workflows that help engineering teams at Woven by Toyota and the broader Toyota organization move faster while keeping safety, security, and quality as top priorities.
 
As part of this function, the Generative AI Engineering team builds enterprise-grade AI platforms and developer-facing AI capabilities. We focus on secure access to large language models, AI gateways, internal AI tooling, model usage governance, identity-aware authorization, cost controls, observability, and integrations with engineering systems.
 
Our goal is to make AI useful, reliable, and safe at enterprise scale.

WHO ARE WE LOOKING FOR?

We are looking for an Engineering Manager to lead a high-impact Generative AI Engineering team and deliver enterprise-grade AI platform capabilities for Developer Productivity. This role requires experience leading complex engineering projects, partnering closely with engineers and stakeholders, and identifying practical accelerators that improve how engineering teams build, test, operate, and deliver software.

The ideal candidate is deeply focused on customer value and eager to understand the real problems engineers are trying to solve. They will help the team clarify requirements, make sound technical decisions, align on software design, and deliver reliable platform capabilities that can be adopted across teams.

Success in this role requires the ability to set direction while remaining close to implementation details. The Engineering Manager will define goals, prioritize work, reduce unnecessary rework, and ensure the team’s roadmap aligns with the broader Developer Experience and Generative AI strategy. They will use metrics to assess adoption, usage, quality, reliability, and business value, continuously improving the team’s impact over time.

A strong understanding of current trends in developer productivity, platform engineering, and enterprise Generative AI is essential, along with the ability to translate that understanding into a clear and motivating vision for the team.

This position reports to the Head of Developer Experience and Generative AI. The Engineering Manager will work closely with them to shape strategy, guide execution, and help the team deliver safe, reliable, and useful AI capabilities at enterprise scale. 

RESPONSIBILITIES

Define and lead the technical direction for enterprise Generative AI platform capabilities within Developer Productivity
Manage and guide the Generative AI Engineering team, working closely with the Head of Developer Experience and Generative AI, engineers, product partners, security teams, identity teams, and internal stakeholders
Break down ambiguous, high-level problems into clear technical plans, milestones, and deliverables that the team can execute
Own delivery of team commitments, including prioritization, planning, dependency management, progress tracking, and communication of risks or trade-offs
Lead the team in building secure, reliable, observable, and maintainable AI platform services, including integrations with AI gateways, LLM providers, identity systems, developer tools, and enterprise systems
Define and track metrics for adoption, usage, quality, reliability, cost, and business value of the team’s capabilities
Support organizational health through goal setting, regular feedback, progress monitoring, coaching, and growth of team members
Manage technical debt and architectural consistency to support long-term maintainability and sustainable delivery
Stay close to the team’s technical work by contributing to design discussions, reviewing technical proposals, and occasionally contributing directly to implementation when appropriate
Identify and address technical challenges, delivery risks, operational issues, and cross-team blockers
Improve engineering excellence across the team by encouraging strong practices for automated testing, CI/CD, code review, observability, documentation, security, and maintainable software design

MINIMUM QUALIFICATIONS

B.S. or M.S. in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience
7+ years of professional software engineering experience, including 3+ years managing or leading a technical team
Experience leading delivery of complex software platforms, developer tools, infrastructure systems, or enterprise internal tools
Experience managing engineers through planning, execution, feedback, coaching, performance management, and technical decision-making
Experience with CI/CD, test automation, infrastructure as code, cloud infrastructure, containers, and production deployment workflows
Experience building or operating production systems that use LLM APIs, model providers, or Generative AI capabilities
Understanding of production concerns for GenAI systems, including security, access control, observability, cost management, evaluation, reliability, and data protection
Experience working with cross-functional stakeholders such as product, security, identity, platform engineering, and internal customer teams
Strong engineering fundamentals, including system design, software architecture, operational excellence, incident response, and maintainable software design
Ability to communicate clearly in English, including explaining technical trade-offs, risks, and priorities to both technical and non-technical stakeholders

NICE TO HAVES

Experience with AI gateway or LLM proxy technologies such as Envoy AI Gateway, Kong AI Gateway, LiteLLM Proxy, OpenAI-compatible gateways, or equivalent systems
Experience with Model Context Protocol, MCP servers or clients, enterprise authorization patterns, or tool permission models
Experience leading platform engineering, developer tools, internal tools, or enterprise self-service platform teams
Experience with enterprise identity, access management, policy enforcement, audit logging, identity-aware proxies, API gateways, or zero-trust architecture
Experience with production GenAI patterns such as retrieval-augmented generation, agentic workflows, tool use, or LLM evaluation frameworks
Ability to communicate in Japanese