Description

• 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:
Qualifications/ What you bring (Must Haves) – Highlight Top 3-5 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 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 connectiv ity

Added bonus if you have (Preferred):

• 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

Key Skills

Education

Bachelor's degree