MLOps Platform Engineer (SageMaker)

Job Description 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 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
• 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

Meet Your Recruiter Peter Jackson TPI Global (formerly Tech Providers, Inc.)