MLOps Platform Engineer (SageMaker)

Job Title: MLOps Platform Engineer (SageMaker) Job Location: Plano, TX Project Duration: 12 months with possible extension Job Summary What we're looking for Client is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker 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. What 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 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 - Unified Studio is preferred to have but Classic is must have. - 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 connectivity 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