Lead AI/ML Platform Engineer
AI summary of the role
Lead the design and build of scalable AI/ML and GenAI platform infrastructure for Toyota Financial Services.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Design and implement cloud-native infrastructure for enterprise AI/ML and GenAI workloads in production
- Build and evolve MLOps and LLMOps platform capabilities including model training, versioning, deployment, monitoring, and rollback
- Create GPU-accelerated compute environments balancing scalability and cost efficiency
- Standardize infrastructure patterns for vector databases, model registries, and orchestration frameworks
What you’ll bring
- 10+ years in software engineering with focus on cloud infrastructure or cloud platform engineering
- 3+ years building cloud infrastructure supporting AI/ML workloads (training, tuning, inference)
- Deep hands-on experience with AWS and infrastructure-as-code (Terraform, CDK, or CloudFormation)
- Experience with Kubernetes, containerization, and CI/CD pipelines in production
Technologies
AWS · Terraform · CDK · CloudFormation · Kubernetes · GitHub Actions · Jenkins · Datadog · CloudWatch · Prometheus
About Toyota
World's largest automaker by volume, building Toyota/Lexus vehicles plus financing (TFS), connected services, motorsports (TRD), and battery manufacturing across a multi-pathway electrification strategy.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
job applicants for employment-based visas or any other work authorization for this position at this time. Who we’re looking for Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Lead AI/ML Platform Engineer. The primary responsibility of this role is to design, build, and implement scalable platform solutions that power enterprise AI/ML and GenAI capabilities across the organization. You will help enable secure, production-ready MLOps and LLMOps infrastructure that supports model training, inference, orchestration, and retrieval-augmented generation. The Lead AI/ML Platform Engineer will support the Enterprise Platforms team’s objective to deliver reliable, secure, and high-performing AI platform capabilities that drive business value at scale. What you’ll be doing In this role, you’ll help shape the foundation for Toyota Financial
More from the job description
Overview Who we are Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us. An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment. To save time applying, Toyota does not offer sponsorship of job applicants for employment-based visas or any other work authorization for this position at this time. Who we’re looking for Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Lead AI/ML Platform Engineer. The primary responsibility of this role is to design, build, and implement scalable platform solutions that power enterprise AI/ML and GenAI capabilities across the organization. You will help enable secure, production-ready MLOps and LLMOps [... source excerpt omitted ...] p shape the foundation for Toyota Financial Services’ next generation of AI platform capabilities, where success means building systems that are scalable, resilient, and ready for production use. A typical day may include collaborating with product, architecture, engineering, data, and cybersecurity partners to solve complex infrastructure challenges while improving the developer and model lifecycle experience. Design and implement cloud-native infrastructure that enables enterprise AI/ML and GenAI workloads in production Build and evolve MLOps and LLMOps platform capabilities, including model training, versioning, deployment, monitoring, and rollback Create GPU-accelerated comp [... source excerpt omitted ...] eep hands-on experience with AWS and infrastructure-as-code tools such as Terraform, CDK, or CloudFormation Experience with Kubernetes, containerization, and CI/CD pipelines in a production environment Strong understanding of GPU infrastructure, serverless compute, and scalable microservice patterns Familiarity with model hosting, inference scaling, and observability tools such as Datadog, CloudWatch, or Prometheus Practical experience using Git/GitHub and CI/CD tooling such as GitHub Actions or Jenkins Added bonus if you have Experience with AWS AI/ML services such as SageMaker or Bedrock Familiarity with LLMOps tooling and GenAI infrastructure such as LangChain or RAG pipe
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