eTeam
AWS SageMaker MLOps Engineer-Hybrid
eTeam - Atlanta, Georgia, United States, 30383
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Overview
Job Title: AWS SageMaker MLOps Engineer
Work Location & Reporting Address: Atlanta, GA 30303 (Onsite-Hybrid. Will consider candidates willing to relocate to client's location)
Must Have Skills: • AWS Sagemaker, AWS ECR , ML OPS • Terraform • ML OPS, GEN AI, Python
Nice to Have Skills: • IAC • FAST API
Detailed Job Description: 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.
Required skills and experience: • 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.
Minimum Years of Experience: 5+ years
Certifications Needed: No
Top 3 responsibilities you would expect the Subcon to shoulder and execute: • Strong communication skills • Strong programming skills
Interview Process (Is face to face required?) No
Any additional information you would like to share about the project specs/ nature of work:
Must Have Skills: • AWS Sagemaker, AWS ECR , ML OPS • Terraform • ML OPS, GEN AI, Python
Nice to Have Skills: • IAC • FAST API
Detailed Job Description: 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.
Required skills and experience: • 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.
Minimum Years of Experience: 5+ years
Certifications Needed: No
Top 3 responsibilities you would expect the Subcon to shoulder and execute: • Strong communication skills • Strong programming skills
Interview Process (Is face to face required?) No
Any additional information you would like to share about the project specs/ nature of work: