Engineering Manager, Generative AI Engineering

Job description

**About Woven by Toyota** [Woven by Toyota](https://woven.toyota/en) is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society. Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all. ========================================================================= ### 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 who can lead a high-impact Generative AI Engineering team and deliver enterprise-grade AI platform capabilities for Developer Productivity. You should have experience leading complex engineering projects, working closely with engineers and stakeholders, and identifying practical accelerators that improve how engineering teams build, test, operate, and deliver software. You should be deeply focused on customer value and willing to spend time understanding the real problems engineers are trying to solve. You help the team clarify requirements, make sound technical decisions, align on software design, and deliver reliable platform capabilities that can be adopted across teams. You should be comfortable setting direction while staying close to implementation details. You define goals, prioritize work, reduce unnecessary rework, and ensure that the team’s roadmap is aligned with the broader Developer Experience and Generative AI strategy. You use metrics to understand adoption, usage, quality, reliability, and business value, then continuously improve the team’s impact over time. You should understand current trends in developer productivity, platform engineering, and enterprise Generative AI, and be able to turn that understanding into a clear, motivating vision for the team. You will report to the Head of Developer Experience and Generative AI, working closely with them to shape strategy, guide execution, and help the team deliver safe, reliable, and useful AI capabilities at enterprise scale. You will work in a hybrid environment requiring your presence onsite at least 3 days per week. ### 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