Sr. ML Platform Engineer; AWS SageMaker

BEPC has an open position as a Sr. ML Platform Engineer (AWS Sage Maker)

Benefits:
Medical, Dental, Vision, and Life Insurance

Pay Rate: $100.00 - $102.94 Per hour based on experience

Term: 12-month contract with possible extensions or permanency based on performance

Shift: 8:00 AM to 5:00 PM

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

We are seeking a highly experienced Senior ML Platform Engineer to design, build, and operationalize an enterprise Machine Learning platform on AWS Sage Maker 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.

This is a fully onsite position based in Plano, TX. Local candidates are strongly preferred.

• Configure and manage Sage Maker 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 Sage Maker Pipelines for data ingestion, preprocessing, model training, evaluation, and deployment.
• Manage Sage Maker 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 Sage Maker 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 Cloud Watch and Datadog.

• 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 Sage Maker Studio Classic (required).
• 3+ years building and supporting production MLOps environments, including versioning.
• Strong experience with Sage Maker Studio Classic;
  Sage Maker Unified Studio experience preferred.
• Strong experience with MLflow or equivalent experiment tracking platforms.
• Experience with workflow orchestration tools such as Sage Maker Pipelines, Airflow, or AWS Step Functions.
• Infrastructure‑as‑Code expertise using Terraform, AWS CDK, or Cloud Formation.
• 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.

• Sage Maker Unified Studio domain provisioning and blueprint customization.
• Sage Maker Feature Store implementation and management.
• AWS Certified Machine Learning – Specialty certification.
• Experience standardizing enterprise ML projects and governance frameworks.

• 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 Sage Maker‑focused MLOps Platform Engineer with extensive AWS expertise.
• Contract length is 12 months with potential for extension.