AI Platform Engineer (MLOps/LLMOps)

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.

=========================================================================

Toyota is redefining what it means to move . We're challenging the current state of mobility by enhancing the movement of people, goods, information and energy. Centered around three core concepts - A Living Laboratory™, Human-Centered, and Ever Evolving City™ - Woven City serves as a test course for mobility to fulfill our purpose of well-being for all. We do this by bringing together a diverse community of people with a shared passion for the future of mobility to co-create, develop and refine innovative products and services. This cross-section of social infrastructure, mobility, and people provides a unique opportunity for inventors, residents and visitors to interact seamlessly with new technologies throughout daily life in an environment that emulates a real city. For more information about Woven City, please visit: https://www.woven-city.global/

We are looking for an AI Platform Engineer who will enable teams developing and operating AI products to deliver high-quality AI capabilities faster, more safely, and on a sustainable basis. This position is not responsible for developing individual product features. Instead, the mission is to design, implement, and operate the shared platforms, workflows, and guardrails required for AI product development, evaluation, release, and operations. By reducing manual work and reliance on individual knowledge, you will improve development productivity, operational efficiency, and the quality and reliability of AI outputs across the organization. From an MLOps perspective, you will build and improve systems that support reliable operations through the management of models, data, experiments, evaluation results, and releases; CI/CD; infrastructure configuration management; monitoring, log collection, and alerting; and automation of incident response. You will also build mechanisms with validation workflows, policies, and evaluation methodologies, including agent-based code review. From an LLMOps perspective, you will drive the design and operation of feedback loops that collect and organize user feedback, usage logs, AI quality evaluation results, incidents, and failure cases, and use these insights to improve agents and tools. You will be expected not only to experiment with emerging technologies, but also to turn them into solutions that can be continuously used in real development environments, with due consideration for security, quality, reproducibility, and operational sustainability. You will work closely with product development teams, ML engineers, and security and cloud infrastructure stakeholders to evolve a reusable AI Platform that supports multiple products. This position reports to a functional leader. The work arrangement is hybrid, and travel to Woven City several times per month is required.

Design, implement, and operate MLOps/LLMOps platforms and shared workflows Build systems that enable the management and traceability of models, prompts, agent configurations, evaluation data, experiment results, and release information Partner with AI Quality Evaluation Engineers and product development teams to build feedback loops that support evaluation execution, regression evaluation, and release decisions Build and operate mechanisms to visualize and monitor LLM output quality, safety, consistency, latency, cost, and usage Establish testing, monitoring, alerting, and rollback mechanisms to detect quality degradation and safety risks resulting from changes to LLM applications at an early stage Automate the development and operation of AI workloads using CI/CD, Infrastructure as Code, container and cloud platforms, and observability tools such as logs, metrics, and traces Design and implement systems that enable the safe and effective use of agents for code generation, modification, and review, including agent-based code review, validation workflows, policies, and auditability

2+ years of professional experience in MLOps, LLMOps, or Platform Engineering Experience designing, implementing, and operating platforms that support the development, evaluation, deployment, and operation of LLM systems Experience automating development or operational processes using CI/CD, Infrastructure as Code, container technologies, and cloud services Software development experience using Python Business-level proficiency in both English and conversational-level Japanese

Experience building feedback loops for AI quality improvement using user feedback, usage logs, evaluation results, support inquiries, incident information, and related data Experience designing and operating systems that consider cost, latency, rate limits, availability, and security when using LLM APIs Experience building mechanisms for AI safety, security, governance, and auditability Experience designing, introducing, and operating development-support systems that use AI agents for code generation, code review, testing, or related engineering workflows A proactive mindset and demonstrated ability to identify challenges in emerging technical areas and drive them from prototyping through the implementation of operationally sustainable solutions