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# Sr. ML Platform Engineer (AWS SageMaker)

- **Company:** Bepc Inc.
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Salary:** $208,000.0 - $214,115.0
- **Contract:** Temporary to permanent
- **Skills:** Airflow, Amazon Web Services, Cloud Computing, Data Discovery, Data Transformation, Identity and Access Management, Machine Learning, Azure Machine Learning, Security Assertion Markup Language (SAML), Single Sign-On, Software Engineering, Management of Software Versions, Datadog, AWS Cdk, Data Logging, Computer Networking Systems, Okta, Data Ingestion, Autoscaling, Snowflake, Cloudformation, Kubernetes, Data Lineage, Performance Monitor, Machine Learning Operations, Cloudwatch, SailPoint, Terraform, Virtual Private Clouds, Software Version Control
- **Published:** June 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=825a6dbad13307d3

## About the Role

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)

## Description

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.


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