Description

RESPONSIBILITIES

• Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. You will migrate the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, covering the full ML lifecycle from data discovery through model deployment and monitoring.
• Set up SageMaker Unified Studio platform - domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows
• Build MLOps pipelines using SageMaker Pipelines - data extraction from Snowflake, preprocessing, training, evaluation, and model registration
• Manage SageMaker Model Registry - cross-account model promotion, versioning, immutability, and lineage tracking
• Configure MLflow experiment tracking - auto-logging of parameters, metrics, and artifacts
• Set up identity and access management - Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelines
• Build model serving - real-time SageMaker endpoints and batch prediction workflows
• Set up model monitoring - data drift, model drift, performance degradation detection
• Configure data catalog - searchable datasets, access-level visibility, access-request workflows, lineage
• Own platform operations - observability (CloudWatch, Datadog), logging, custom images, instance availability

 
REQUIREMENTS

• The hiring manager is looking to onboard MLOpsPlatform Engineer (SageMaker) who is expert in SageMaker (key skillset) and AWS.
• 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 Classic 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
• MLflow or equivalent experiment tracking
• SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
• Unified Studio is preferred to have but Classic (REQUIRED) - domain/project setup, blueprints, multi-tenant configuration
• Experience with SageMaker Unified Studio or Studio Classic - 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

Key Skills

Education

Any Gradute