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Woven by Toyota

Posted 2 days ago

Open

Software Engineer, Generative AI Engineering

TokyoHybrid

AI Summary

About Woven by ToyotaWoven by Toyota 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 ve

About this role

About Woven by Toyota
Woven by Toyota 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.

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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 a hands-on software engineer who can design, build, and operate enterprise-grade Generative AI platforms and services. You should be comfortable working across backend systems, cloud infrastructure, identity and access management, API gateways, LLM providers, and internal developer tools.
You should understand that production AI systems require more than prompts and chat interfaces. We are looking for someone who can build reliable, secure, observable, and maintainable systems around AI usage, including request routing, authentication, authorization, policy enforcement, auditability, cost controls, evaluation, and operational support.
You should be comfortable working through ambiguity, comparing different approaches, explaining trade-offs clearly, and making pragmatic technical decisions based on user needs, enterprise constraints, and long-term maintainability. You will work closely with software engineers, platform engineers, security teams, identity teams, and internal stakeholders.
You will work in a hybrid environment requiring your presence onsite at least 3 days per week.

RESPONSIBILITIES

  • Build, operate, and improve enterprise Generative AI platform capabilities for Developer Productivity and broader engineering use cases
  • Design and implement backend services, APIs, and integrations that enable secure and reliable use of LLMs across internal tools and workflows
  • Work with AI gateway technologies such as Envoy AI Gateway, Kong AI Gateway, LiteLLM Proxy, or equivalent systems for model routing, policy enforcement, observability, and cost control
  • Integrate GenAI systems with enterprise identity and access management platforms such as Microsoft Entra ID, including authentication, authorization, group-based access, service principals, and audit requirements
  • Build integrations with internal engineering systems, developer tools, enterprise data sources, and MCP servers or clients
  • Contribute to architecture and implementation decisions around MCP, including enterprise authorization patterns and emerging standards such as the Enterprise-Managed Authorization extension for MCP
  • Develop production-grade services using Python, TypeScript, or similar languages, with attention to reliability, testing, observability, and maintainability
  • Contribute to CI/CD pipelines, infrastructure-as-code, cloud resources, Kubernetes workloads, and operational procedures for AI platform services
  • Evaluate GenAI frameworks, model providers, gateway technologies, and emerging standards, then communicate trade-offs clearly to technical and non-technical stakeholders
  • Create clear documentation for platform behavior, integration patterns, security assumptions, operational procedures, and known limitations

    • MINIMUM QUALIFICATIONS

  • 4+ years of professional software engineering experience building and operating production systems
  • Hands-on experience building backend services, APIs, platform services, or developer tools using Python, TypeScript, Java, Go, or similar languages
  • Experience deploying and operating production systems using cloud infrastructure, CI/CD, infrastructure as code, containers, and Kubernetes or equivalent orchestration platforms
  • Experience integrating applications or services with enterprise authentication and authorization systems, such as Microsoft Entra ID, OAuth2/OIDC, SAML, service principals, RBAC, or group-based access control
  • Experience building systems that use LLM APIs or model providers such as OpenAI, Azure OpenAI, Anthropic, Gemini, Vertex AI, Bedrock, or equivalent
  • Understanding of production concerns for GenAI systems, including access control, prompt and request handling, model routing, rate limits, observability, evaluation, cost management, and data protection
  • Strong engineering fundamentals, including system design, testing, debugging, incident response, code review, and maintainable software design
  • Ability to work across teams, ask clear questions, explain trade-offs, and turn ambiguous platform requirements into practical engineering plans

    • 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, including MCP servers, MCP clients, authorization patterns, tool permissions, and enterprise deployment trade offs
  • Familiarity with emerging MCP standards such as the Enterprise-Managed Authorization extension
  • Experience building enterprise developer tools, platform engineering systems, internal tools, or secure self-service platforms
  • Experience with policy enforcement, audit logging, secrets management, identity-aware proxies, API gateways, or zero-trust architecture
  • Experience with retrieval-augmented generation,, agentic workflows, or LLM evaluation frameworks
  • Experience with AWS infrastructure for AI, identity, networking, and platform services
  • Ability to communicate in Japanese

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