AWS Sagemaker

  • Posted 2 days ago | Updated 2 days ago

Overview

Remote
$57 - $60
Contract - W2
Contract - 12 Month(s)

Skills

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.

Job Details

AWS Sagemaker
Location: Remote
Duration: 9-12 Month
Implementing Client: Infosys

JD:

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.


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