MLOps Platform Engineer; SageMaker

Job Title:

MLOps Platform Engineer (Sage Maker)

Job Location:

Plano, TX

Project Duration: 12 months with possible extension

Client 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
• 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 (Cloud Watch, Datadog), logging, custom images, instance availability

• 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
• Unified Studio is preferred to have but Classic is must have.
• 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 Unified Studio domain provisioning, custom blueprints, project standardization
• Sage Maker Feature Store for online/offline feature management
• Sage Maker Model Monitor —data quality checks, bias detection, drift detection
• AWS Machine Learning Specialty certification