Description

You will design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio, migrating the organization from a fragmented toolchain to a unified, governed system. You will own the full ML lifecycle from data discovery through model deployment, monitoring, and serving.

This role is on-site.

Responsibilities

• Set up SageMaker Unified Studio, including domain configuration, project provisioning, persona-based roles, and multi-environment promotion workflows.
• Build MLOps pipelines using SageMaker Pipelines to extract data from Snowflake, preprocess, train, evaluate, and register models.
• Configure MLflow for experiment tracking, auto-logging parameters, metrics, and artifacts.
• Manage SageMaker Model Registry with cross-account promotion, versioning, immutability, and lineage tracking.
• Own platform operations, including observability (CloudWatch, Datadog), identity and access management (Okta SSO, SailPoint), and model serving via real-time endpoints and batch workflows.

Required Skills

• 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 for training, versioning, deployment, monitoring, and rollback.
• Experience with SageMaker Unified Studio or Studio Classic, including domain/project setup, blueprints, and multi-tenant configuration.
• Proficiency with MLflow or equivalent experiment tracking tools.
• Experience with SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions).
• Infrastructure-as-Code with Terraform, CDK, or CloudFormation.
• IAM design for ML platforms, including execution roles, service roles, cross-account access, Lake Formation, and SSO/SAML.

Preferred Skills

• Experience with Kubernetes (EKS) and container orchestration.

Key Skills

Education

Bachelor's degree