> Markdown version of [/jobs/ext/3812307-senior-machine-learning-platform-engineer](https://www.wearedevelopers.com/jobs/ext/3812307-senior-machine-learning-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Machine Learning Platform Engineer

- **Company:** Toyota Financial Services
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Data Discovery, Data Transformation, Data Mining, DevOps, Identity and Access Management, Machine Learning, Azure Machine Learning, Software Deployment, Software Engineering, Management of Software Versions, Datadog, Data Logging, Feature Store, Okta, Snowflake, Cloudformation, Infrastructure Automation Frameworks, Machine Learning Operations, Drift Detection, Model Registry, Cloudwatch, SailPoint, Terraform
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/64534d3f-60a8-4b06-8e4e-2fd6ad5a8838

## About the Role

Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Senior Machine Learning Platform Engineer. The primary responsibility of this role is to design, build, and operationalize an enterprise-grade ML platform on AWS SageMaker AI or Unified Studio. You will contribute to the organization's migration from a fragmented ML toolchain to a unified, governed environment, supporting how we handle the full ML lifecycle from data discovery to production deployment and monitoring. Reporting to the Enterprise Platforms leadership, the person in this role will support the team's objective to scale our ML infrastructure and empower data teams to deliver high-impact AI solutions with speed and reliability., * Bachelor's degree in a relevant field that provides a strong foundation in software engineering, cloud platforms, or machine learning.

 * 4+ years of DevOps or Software Engineering experience in cloud infrastructure or ML platform operations.
 * 2+ years of hands-on AWS experience, including Amazon SageMaker Studio, Pipelines, Model Registry, Endpoints, and Feature Store.
 * 2+ years of experience building and operating production MLOps pipelines, including training, versioning, deployment, and rollback strategies.
 * Proficiency with infrastructure-as-code tools such as Terraform, CDK, or CloudFormation to build repeatable, and scalable environments.
 * Working understanding of IAM design for ML, including execution roles, service roles, and cross-account access management.
 * Strong collaboration and communication skills, with the ability to work independently while partnering effectively across teams.

Added bonus if you have

 * Advanced knowledge of SageMaker AI, including domain provisioning, custom blueprints, and project standardization.
 * An AWS Machine Learning Specialty certification that demonstrates deeper technical expertise.

## Description

* Design, build, and enhance cloud-native MLOps platform components for production ML workloads, including SageMaker domain configuration, project provisioning, and persona-based roles.
 * Build SageMaker Pipelines for Snowflake data extraction, preprocessing, training, evaluation, and model registration; configure MLflow auto-logging for parameters, metrics, and artifacts.
 * Manage SageMaker Model Registry, including cross-account promotion, versioning, immutability, and lineage; support real-time SageMaker endpoints and batch prediction workflows.
 * Implement identity and access management with Okta SSO, SailPoint entitlements, persona-based execution roles, and pipeline service roles.
 * Set up model monitoring for data drift, model drift, and performance degradation, and configure a data catalog with searchable datasets, access visibility, request workflows, and lineage.
 * Support platform operations through CloudWatch and Datadog observability, logging, custom images, and instance availability; troubleshoot production issues, perform root cause analysis, and drive improvements.
 * Stay current with evolving AI/ML platform technologies and engineering best practices.