What are the responsibilities and job description for the Urgent Needed - Machine Learning Engineer - Atlanta, GA position at SATCON Inc?
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
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 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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