Senior MLOps Platform Engineer

Role: Senior MLOps Platform Engineer

Location: Plano, TX (Onsite)

 

 

This position is with Enterprise Analytical Data & Integration Team and the hiring manager is looking to onboard MLOps Platform Engineer (Sagemaker) who is expert in Sagemaker and AWS (key skillsets).

Local candidates preferred, 12 months contract with extension, Onsite role.

 

Must Haves:

10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations.

5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio Classic Studio, Pipelines, Model Registry, Endpoints, Feature Store)

3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback

Experience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration

MLflow or equivalent experiment tracking

SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)

Unified Studio is preferred to have but Classic is must have.

 

 

Interview Process:

1st Round- MS Teams - Technical Interview – SageMaker and AWS

2nd Round- MS Teams - Technical Interview – SageMaker and AWS

 

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 diverse, talented team members who want to Dream. Do. Grow. with us.

What we’re looking for

Toyota Financial Services Enterprise Platforms team is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. You will migrate the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, covering the full ML lifecycle from data discovery through model deployment and monitoring.

What you’ll be doing

- Set up SageMaker Unified Studio platform — domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows

- Build MLOps pipelines using SageMaker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, and model registration

- Manage SageMaker Model Registry — cross-account model promotion, versioning, immutability, and lineage tracking

- Configure MLflow experiment tracking — auto-logging of parameters, metrics, and artifacts

- Set up identity and access management — Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelines

- Build model serving — real-time SageMaker endpoints and batch prediction workflows

- Set up model monitoring — data drift, model drift, performance degradation detection

- Configure data catalog — searchable datasets, access-level visibility, access-request workflows, lineage

- Own platform operations — observability (CloudWatch, Datadog), logging, custom images, instance availability

 

Requirements:

Qualifications/ What you bring (Must Haves) – Highlight Top 3-5 skills

 

- 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations

- 5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)

- 3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback

- Experience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration

- Infrastructure-as-Code with Terraform, CDK, or CloudFormation

- IAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAML

- MLflow or equivalent experiment tracking

- SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)

- Model serving — real-time endpoints, batch transform, auto-scaling, endpoint monitoring

- Snowflake as a data source for ML pipelines

- Kubernetes (EKS) and container orchestration

- Networking and security — VPC, security groups, private endpoints, cross-account connectivity

 

Added bonus if you have (Preferred):

- SageMaker Unified Studio domain provisioning, custom blueprints, project standardization

- SageMaker Feature Store for online/offline feature management

- SageMaker Model Monitor — data quality checks, bias detection, drift detection

- AWS Machine Learning Specialty certification