Description

You will build and operate the MLOps platform on AWS SageMaker, managing infrastructure, pipelines, and model serving for production environments.

This role is on-site.

Responsibilities

• Set up and configure SageMaker Unified Studio or Studio Classic, including domain setup, project provisioning, and multi-tenant configuration.
• Build and manage MLOps pipelines using SageMaker Pipelines for data extraction from Snowflake, preprocessing, training, evaluation, and model registration.
• Configure identity and access management with Okta SSO and SailPoint, defining persona-based execution roles and service roles for pipelines.
• Implement model serving via real-time endpoints and batch workflows, including auto-scaling and endpoint monitoring.
• Set up model monitoring for data and model drift, and configure MLflow for experiment tracking and artifact logging.

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, including training, versioning, deployment, and rollback.
• Experience with Infrastructure-as-Code using Terraform, CDK, or CloudFormation.
• Strong knowledge of IAM design for ML platforms, including cross-account access, Lake Formation, and SSO/SAML.
• Proficiency in SageMaker Pipelines or similar workflow orchestration tools like Airflow or Step Functions.
• Experience with Kubernetes (EKS) and container orchestration.
• Understanding of networking and security, including VPC, security groups, and private endpoints.

Preferred Skills

• Experience with SageMaker Unified Studio domain provisioning, custom blueprints, and project standardization.
• Knowledge of SageMaker Feature Store for online/offline feature management.

Key Skills

Education

Any Graduate