AWS Sagemaker Data Science

Summary: The AWS SageMaker Data Science role focuses on assessing and migrating machine learning models and workflows to AWS SageMaker. The position requires designing migration strategies, optimizing model deployment, and implementing MLOps best practices. Collaboration with data scientists and engineers is essential to ensure successful integration and resource optimization. The role emphasizes staying updated with the latest SageMaker features and practices.

Key Responsibilities:

• Assess existing machine learning models, workflows, and infrastructure (Python (Anaconda) for migration to AWS SageMaker.
• Design and implement migration strategies for on-premises, other cloud platforms, or older SageMaker environments to target SageMaker services.
• Leverage various SageMaker services, such as SageMaker Studio, Pipelines, Model Registry, and Endpoints, to streamline the ML lifecycle and model deployment.
• Prepare and validate data for training and inference within SageMaker.
• Containerize models and dependencies using Docker and AWS ECR for efficient deployment on SageMaker.
• Develop and optimize inference scripts for various model types within SageMaker endpoints.
• Configure and deploy SageMaker endpoints for real-time and batch predictions, ensuring high availability and scalability.
• Implement MLOps best practices within SageMaker, including automated model deployment, monitoring, and versioning.
• Troubleshoot and debug issues during migration and post-migration phases.
• Collaborate with data scientists, software engineers, and other stakeholders to ensure successful migration and integration of models.
• Optimize resource utilization and costs related to SageMaker deployments.
• Stay updated with the latest SageMaker features and best practices.

Key Skills:

• Strong understanding of machine learning concepts and lifecycle.
• In-depth knowledge and hands-on experience with AWS SageMaker services, including Studio, Terraform Pipelines, Model Registry, Training, and Endpoints.
• Experience with Terraform/ Lambda and containerization for ML model deployment.
• Experience with migrating ML models from diverse environments to AWS SageMaker.
• Familiarity with AWS services like S3, ECR, Lambda, and IAM for supporting SageMaker workloads.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT