Lead AI/ML Platform Engineer

Overview

Who we are

Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, TN, (job flexibility benefits) (also known as I-140 or Adjustment of Status portability), etc.) now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future.

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 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 compute environments that improve model performance while balancing scalability and cost efficiency

• Standardize infrastructure patterns for vector databases, model registries, and orchestration frameworks

• Develop reusable approaches for model serving, inference scaling, prompt management, and latency optimization

• Design secure, multi-tenant environments with strong access controls, auditability, and usage governance for AI models

• Partner closely with engineering, platform, and data teams to ensure smooth data flow, strong observability, and operational resiliency

• Own technical direction for AI infrastructure services and integrations in collaboration with the architecture team

• Lead design reviews, establish engineering standards, and help guide critical technical decisions

• Mentor engineers, provide thoughtful feedback, and support growth through coaching and development planning

• Stay current on emerging GenAI, distributed systems, and infrastructure trends to bring fresh ideas and better solutions to the team

What you bring

• 10+ years of experience in software engineering, with a focus on cloud infrastructure or cloud platform engineering

• 3+ years of experience building cloud infrastructure that supports AI/ML workloads such as training, tuning, and inference

• Deep 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 pipelines

• Experience working with vector databases, model registries, or orchestration tools such as MLflow, Airflow, or Ray

• Knowledge of prompt management, token usage optimization, and model performance tuning

• AWS Solutions Architect Professional or Machine Learning certification

What we’ll bring
During your interview process, we’ll share details about our industry-leading benefits and career development opportunities designed to support your growth and well-being:

• A collaborative work environment built on teamwork, flexibility, and respect

• Professional growth programs including tuition reimbursement to advance your career

• Team Member Vehicle Purchase Discount and Lease Vehicle Program (if applicable)

• Comprehensive health care and wellness plans for you and your family

• Toyota 401(k) Savings Plan with company match plus annual retirement contributions regardless of your participation

• Paid holidays and paid time off for work-life balance

• Referral services for prenatal care, adoption, childcare, schooling, and more

• Tax-advantaged accounts including Health Savings Account (HSA), Health Care FSA, and Dependent Care FSA

Belonging at Toyota

Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10+ different Business Partnering Groups across 100 different North American chapter locations that support team members’ efforts to dream, do and grow without questioning that they belong.

Applicants for our positions are considered without regard to race, ethnicity, national origin, sex, sexual orientation, gender identity or expression, age, disability, religion, military or veteran status, or any other characteristics protected by law.

Have a question, need assistance with your application or do you require any special accommodations? Please send an email to talent.acquisition@toyota.com.