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