SageMaker MLOps Platform Engineer

Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano TX (Onsite)
Duration: 12 Months

Description:

RM Notes:

• Export Control form will be required during onboarding only and is not required at the time of submission.
• This position is with the Enterprise Analytical Data & Integration Team.
• The hiring manager is looking to onboard an experienced MLOps Platform Engineer with strong expertise in AWS and Amazon SageMaker.
• Local candidates are preferred.
• 12-month contract with possible extension.
• Onsite role.

Must-Have Skills

• 10 15 years of software engineering experience focused on cloud infrastructure or ML platform operations.
• 5 years of hands-on AWS experience including deep expertise in Amazon SageMaker (Studio Classic/Studio Pipelines Model Registry Endpoints Feature Store).
• 3 years of experience building and operating production MLOps pipelines including training versioning deployment monitoring and rollback.
• Experience with SageMaker Unified Studio or Studio Classic including domain/project setup blueprints and multi-tenant configuration.
• MLflow or equivalent experiment tracking tools.
• SageMaker Pipelines or similar workflow orchestration tools (Airflow Step Functions).
• SageMaker Unified Studio experience is preferred; Studio Classic experience is mandatory.

What Were Looking For

Toyota Financial Services Enterprise Platforms team is seeking a Senior ML Platform Engineer to design build and operationalize an enterprise ML platform on AWS SageMaker Unified Studio.

The selected candidate will help migrate the organization from a fragmented ML toolchain to a unified governed platform on AWS Landing Zone 2 supporting the complete machine learning lifecycle-from data discovery through model deployment and monitoring.

Key Responsibilities

• Set up SageMaker Unified Studio platform including domain configuration project provisioning persona-based roles and multi-environment (Dev Prod-UAT Prod) promotion workflows.
• Build MLOps pipelines using SageMaker Pipelines for data extraction from Snowflake preprocessing training evaluation and model registration.
• Manage SageMaker Model Registry including cross-account model promotion versioning immutability and lineage tracking.
• Configure MLflow experiment tracking with auto-logging of parameters metrics and artifacts.
• Set up identity and access management including Okta SSO SailPoint entitlements persona-based execution roles and service roles for pipelines.
• Build model serving solutions using real-time SageMaker endpoints and batch prediction workflows.
• Implement model monitoring for data drift model drift and performance degradation detection.
• Configure data catalog capabilities including searchable datasets access-level visibility access-request workflows and lineage tracking.
• Own platform operations including observability (CloudWatch Datadog) logging custom images and instance availability management.

Required Qualifications

Qualifications / What You Bring (Must-Haves)

• 10 15 years of software engineering experience focused on cloud infrastructure or ML platform operations.
• 5 years of hands-on AWS experience with strong expertise in:
  • Amazon SageMaker Studio
  • SageMaker Pipelines
  • Model Registry
  • Endpoints
  • Feature Store
• 3 years of experience building and operating production MLOps pipelines including training versioning deployment monitoring and rollback.
• Experience with SageMaker Unified Studio or Studio Classic including domain/project setup blueprints and multi-tenant configurations.
• Infrastructure-as-Code experience using Terraform CDK or CloudFormation.
• IAM design for ML platforms including execution roles service roles cross-account access Lake Formation and SSO/SAML.
• MLflow or equivalent experiment tracking platform experience.
• SageMaker Pipelines or similar orchestration frameworks (Airflow Step Functions).
• Experience with model serving including real-time endpoints batch transform auto-scaling and endpoint monitoring.
• Experience using Snowflake as a data source for ML pipelines.
• Kubernetes (EKS) and container orchestration experience.
• Strong understanding of networking and security concepts including VPCs security groups private endpoints and cross-account connectivity.

Preferred Qualifications

• SageMaker Unified Studio domain provisioning custom blueprints and project standardization.
• SageMaker Feature Store for online/offline feature management.
• SageMaker Model Monitor including data quality checks bias detection and drift detection.
• AWS Machine Learning Specialty Certification.