Senior ML Platform Engineer - AWS SageMaker & MLOps

Toyota North America; Plano (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health care plans

401(k) with company match

Relocation assistance

Tuition reimbursement

Paid holidays & PTO

Job summary

Toyota Financial Services Enterprise Platforms team is seeking a Senior Machine Learning Platform Engineer to design, build, and operate an enterprise-grade ML platform on AWS SageMaker Studio or Unified Studio. You will help migrate to a unified, scalable ML environment across data discovery, training, deployment, and monitoring.

You’ll collaborate across teams to implement IAM, Okta SSO, and pipeline roles, and ensure observability with CloudWatch and Datadog.

Qualifications

• Minimum 4+ years in DevOps or software engineering for cloud/MLOps.
• Hands-on AWS experience including SageMaker Studio and endpoints.
• Experience building production ML pipelines and model management.

Responsibilities

• Design and operate cloud-native MLOps components for production ML workloads.
• Build SageMaker Pipelines for data extraction, training, evaluation, and model registry.
• Manage SageMaker Model Registry and endpoints, ensure monitoring and lineage.
• Implement IAM with Okta SSO, cross-account access, and execution roles.
• Set up data catalogs, observability with CloudWatch/Datadog, and root-cause analysis.

Skills

DevOps experience

AWS experience

MLOps pipelines

SageMaker proficiency

Infrastructure as code

IAM design

Team collaboration

Communication

Education

Bachelor's degree in related field

Tools

Terraform

CDK

CloudFormation

SageMaker Pipelines

Job description

Toyota Financial Services Enterprise Platforms team is seeking a Senior Machine Learning Platform Engineer to design, build, and operate an enterprise-grade ML platform on AWS SageMaker Studio or Unified Studio. You will help migrate to a unified, scalable ML environment across data discovery, training, deployment, and monitoring.

You’ll collaborate across teams to implement IAM, Okta SSO, and pipeline roles, and ensure observability with CloudWatch and Datadog.