Urgent Needed - ML Engineer with AWS SageMaker - Atlanta, GA - Hybrid

  • Atlanta, GA
  • Posted 8 hours ago | Updated 8 hours ago

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 12 Month(s)

Skills

Machine Learning
Sagemaker

Job Details

Hi,

Our client is looking for Machine Learning Engineer for Atlanta, GA. If you are looking for a job change, please let me know.

Job Title: Machine Learning Engineer AWS SageMaker

Location: Atlanta, GA (onsite)

Duration: Long Term

Employment Type: Contract

Job Description:

  • 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 SageM. Leverage various SageMaker services, such as SageMaker Studio, Pipelines, Model Registry, and Endpoints, to streamline the ML lifecycle.
  • 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 Sage Maker 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, Terra Pipelines, Model Registry, Training
  • Experience with Terraform/Lambida and containerization for ML model deployment.
  • Experience with migrating ML models from diverse environments to AWS SageMaker,
  • Familiarity with AWS services like 53, ECR, Lambda, and IAM for supporting SageMaker workloads
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