MLOps Platform Engineer (SageMaker)

.entry-header Location: TX Type: Contract Job #104903 Job Title: MLOps Platform Engineer (SageMaker) Location: Plano, TX. Job Type: W2 CONTRACT | NO C2C Expected hours per week: 40 hours per week Schedule: Onsite Pay Range: $85-95 an hour Job Description: StartFragment Senior ML Platform Engineer (AWS SageMaker) We’re seeking a Senior ML Platform Engineer to design, build, and support an enterprise-scale machine learning platform focused on AWS SageMaker and MLOps. This role will drive the migration from a fragmented ML ecosystem to a unified, governed platform supporting the full machine learning lifecycle—from data discovery and model development through deployment, monitoring, and operations. What You’ll Do Configure and support AWS SageMaker environments, including domain setup, project provisioning, role-based access, and multi-environment promotion workflows. Build and maintain MLOps pipelines for data ingestion, preprocessing, model training, evaluation, deployment, and monitoring. Manage model versioning, governance, and promotion processes using Model Registry capabilities. Implement experiment tracking and ML lifecycle management using MLflow or similar tools. Build and support real-time and batch model serving solutions. Configure model monitoring, drift detection, and performance tracking. Develop infrastructure using Infrastructure-as-Code tools such as Terraform, CDK, or CloudFormation. Partner with data science, engineering, security, and platform teams to deliver scalable ML solutions. Support platform operations, observability, logging, performance monitoring, and availability. Required Qualifications 10-15 years of software engineering experience focused on cloud infrastructure, platform engineering, or machine learning operations. 5+ years of hands-on AWS experience. Deep expertise with Amazon SageMaker, including: Studio Classic (required) Pipelines Model Registry Endpoints Feature Store 3+ years building and operating production MLOps pipelines. Experience with model training, deployment, versioning, monitoring, and rollback strategies. Experience with SageMaker Studio Classic; Unified Studio experience is highly preferred. Experience with MLflow or equivalent experiment tracking tools. Hands-on experience with SageMaker Pipelines, Airflow, Step Functions, or similar orchestration tools. Infrastructure-as-Code expertise using Terraform, CDK, or CloudFormation. Strong IAM, security, and access management experience. Experience with Snowflake as a source for ML pipelines. Kubernetes (EKS) and containerization experience. Strong understanding of networking, security groups, VPCs, private endpoints, and cross-account connectivity. Preferred Qualifications Experience with SageMaker Unified Studio. Experience with SageMaker Feature Store. Experience with SageMaker Model Monitor, drift detection, and data quality monitoring. AWS Machine Learning Specialty Certification. Experience implementing enterprise-scale governance and standardization for ML platforms. .entry-content .clear #post-##