Job Title: MLOps Platform Engineer (SageMaker) Location: Plano TX Onsite Duration: 12-month contract with possible extension Interview Process: 1st RoundMS Teams Technical Interview - SageMaker and AWS 2nd RoundMS Teams Technical Interview - SageMaker and AWS Must Haves: 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 Classic 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 MLflow or equivalent experiment tracking SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions) Unified Studio is preferred to have but Classic is must have. What we're looking for Enterprise Platforms team 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 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 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 certificationPDN-a22a4fff-63b1-4f8a-a266-20984538f10b