Sr. ML Platform Engineer (AWS SageMaker)

We are seeking a highly experienced Senior ML Platform Engineer to design, build, and operationalize an enterprise Machine Learning platform on AWS SageMaker Unified Studio. This role will lead the migration from a fragmented ML ecosystem to a unified, governed platform running on AWS Landing Zone 2, supporting the complete ML lifecycle from data discovery and experimentation through deployment, monitoring, and governance., · Configure and manage SageMaker Unified Studio environments, including domain setup, project provisioning, persona-based access controls, and multi-environment promotion workflows (Dev, UAT, Prod).

· Design and implement enterprise-grade MLOps pipelines using SageMaker Pipelines for data ingestion, preprocessing, model training, evaluation, and deployment.

· Manage SageMaker Model Registry, including model versioning, cross-account promotion, lineage tracking, and governance.

· Implement MLflow experiment tracking with automated logging of metrics, parameters, and artifacts.

· Configure and maintain identity and access management integrations including Okta SSO, SailPoint, IAM roles, and service accounts.

· Develop scalable model serving solutions using SageMaker Endpoints and batch inference workflows.

· Establish model monitoring frameworks for drift detection, data quality validation, and performance monitoring.

· Configure enterprise data catalog capabilities with lineage tracking and governed access workflows.

· Support platform operations, observability, logging, monitoring, custom container images, and infrastructure optimization using CloudWatch and Datadog.

Do you have a valid AWS Certified Machine Learning - Specialty certification?, Do you have experience in Virtual Private Clouds?, Do you have a Bachelor’s degree?, Requirements: Bachelor’s Degree / 10-15 years of software engineering experience focused on cloud infrastructure, platform engineering, or ML platform operations. / 5+ years of hands-on AWS experience / 3+ years building and supporting production MLOps environments, · 10-15 years of software engineering experience focused on cloud infrastructure, platform engineering, or ML platform operations.

5+ years of hands-on AWS experience with deep expertise in:

· Amazon SageMaker Studio Classic (required)

· SageMaker Pipelines

· SageMaker Model Registry

· SageMaker Endpoints

· SageMaker Feature Store

3+ years building and supporting production MLOps environments, including:

· Model training

· Versioning

· Deployment

· Monitoring

· Rollback strategies

· Experience with SageMaker Studio Classic (required); SageMaker Unified Studio experience preferred.

· Strong experience with MLflow or equivalent experiment tracking platforms.

· Experience with workflow orchestration tools such as SageMaker Pipelines, Airflow, or AWS Step Functions.

· Infrastructure-as-Code expertise using Terraform, AWS CDK, or CloudFormation.

· Experience designing IAM architectures for ML platforms, including cross-account access, SSO/SAML integrations, and Lake Formation.

· Experience with model serving, endpoint monitoring, batch inference, and auto-scaling.

· Experience integrating Snowflake as a data source for machine learning workflows.

· Kubernetes (EKS) and container orchestration experience.

· Strong networking and security knowledge including VPCs, security groups, private endpoints, and cross-account connectivity.

Preferred Qualifications:

· SageMaker Unified Studio domain provisioning and blueprint customization.

· SageMaker Feature Store implementation and management.

· SageMaker Model Monitor experience for data quality, bias detection, and drift monitoring.

· AWS Certified Machine Learning - Specialty certification.

· Experience standardizing enterprise ML projects and governance frameworks.

· Additional Information

· Local candidates in the Plano, TX area are highly preferred.

· Export Control documentation will be required during onboarding (not required during submission).

· Seeking a strong SageMaker-focused MLOps Platform Engineer with extensive AWS expertise., * Bachelor’s (Required), * Cloud infrastructure: 10 years (Required)

• ML platform Ops: 10 years (Required)
• AWS / Sagemaker: 5 years (Required)

$100.00 - $102.94 an hour - Full-time, Contract, Pulled from the full job description

• Health insurance
• Vision insurance
• Dental insurance
• Life insurance, * Dental insurance
• Health insurance
• Life insurance
• Vision insurance