MLOps Platform Engineer (SageMaker)

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 connectivityAdded 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 certificationWhat you'll be doing - Set up SageMaker Unified Studio platform - domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflowsBuild MLOps pipelines using SageMaker Pipelines - data extraction from Snowflake, preprocessing, training, evaluation, and model registrationManage SageMaker Model Registry - cross-account model promotion, versioning, immutability, and lineage trackingConfigure MLflow experiment tracking - auto-logging of parameters, metrics, and artifactsSet up identity and access management - Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelinesBuild model serving - real-time SageMaker endpoints and batch prediction workflowsSet up model monitoring - data drift, model drift, performance degradation detectionConfigure data catalog - searchable datasets, access-level visibility, access-request workflows, lineageOwn platform operations - observability (CloudWatch, Datadog), logging, custom images, instance availability