Woven By Toyota2mo ago

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Software Engineer, Generative AI Engineering

Japan·TokyoEmployeemid

Software EngineerSoftware Engineering

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Overview

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,

Technical Tools

Software EngineerSoftware Engineering

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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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.

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.

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

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

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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Important Points

・All interviews will be arranged via Google Meet, unless otherwise stated.

・The same job descriptions are available in both English and Japanese; therefore, we kindly ask that you apply to only one version.

・We kindly request that you submit your resume in English, if possible. However, Japanese resumes are also acceptable. Please note that, depending on the English proficiency requirements of the role, we may request an English version of your resume later in the process.

WHAT WE OFFER

・Competitive Salary - Based on experience

・Work Hours - Flexible working time

・Paid Holiday - 20 days per year (prorated)

・Sick Leave - 6 days per year (prorated)

・Holiday - Sat & Sun, Japanese National Holidays, and other days defined by our company

・Japanese Social Insurance - Health Insurance, Pension, Workers’ Comp, and Unemployment Insurance, Long-term care insurance

・Housing Allowance

・Retirement Benefits

・Rental Cars Support

・In-house Training Program (software study/language study)

Our Commitment