MLOps Platform Engineer; SageMaker

Client Enterprise Platforms team is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS Sage Maker 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 Sage Maker Unified Studio platform — domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows
• Build MLOps pipelines using Sage Maker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, and model registration
• Manage Sage Maker 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 Sage Maker endpoints and batch prediction workflows
• Configure data catalog — searchable datasets, access-level visibility, access-request workflows, lineage
• Own platform operations — observability (Cloud Watch, Datadog), logging, custom images, instance availability

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 Sage Maker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)
• 3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback
• Experience with Sage Maker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration
• Infrastructure-as-Code with Terraform, CDK, or Cloud Formation
• IAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAML
• MLflow or equivalent experiment tracking
• Sage Maker 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 connectivity

• Sage Maker Feature Store for online/offline feature management
• AWS Machine Learning Specialty certification

• 1st Round- MS Teams
  - Technical Interview – Sage Maker and AWS
• 2nd Round- MS Teams
  - Technical Interview – Sage Maker and AWS