MLOps Platform Engineer (SageMaker)

10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations

5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)

3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback

Experience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration

Infrastructure-as-Code with Terraform, CDK, or CloudFormation

IAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAML

MLflow or equivalent experiment tracking

SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)

Model serving — real-time endpoints, batch transform, auto-scaling, endpoint monitoring

Snowflake as a data source for ML pipelines

Kubernetes (EKS) and container orchestration

Networking and security — VPC, security groups, private endpoints, cross-account connectivity

SageMaker Unified Studio domain provisioning, custom blueprints, project standardization

SageMaker Feature Store for online/offline feature management

SageMaker Model Monitor — data quality checks, bias detection, drift detection

AWS Machine Learning Specialty certification