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:
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.
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