Sr. ML Platform Engineer (AWS SageMaker)

BEPC has an open position as a Sr. ML Platform Engineer (AWS SageMaker) Location: Plano, TX 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 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 NO CORP-TO-CORP CANDIDATES WILL BE CONSIDERED Position Overview: 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. This is a fully onsite position based in Plano, TX . Local candidates are strongly preferred. Key Responsibilities: · 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. Required Qualifications: · 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. · Contract length is 12 months with potential for extension.