| AWS Machine Learning Consultant at Remote, Remote, USA |
| Email: [email protected] |
| From: Vicky, istaffx [email protected] Reply to: [email protected] Remote AWS Machine Learning Consultant with strong experience in AWS SageMaker (for Machine Learning) to join their team. This resource will work hands-on with the team to develop technical solutions that drive innovation and help grow the accounts across the Department of Health and Human Services (HHS) Health Resources and Services Administration (HRSA). REQUIREMENTS: 5+ years of hands-on technical experience within AWS analytics utilizing SageMaker for Machine Learning Including experience building, training, and deploying ML models into production Extensive experience in a data science/machine learning consulting position exposure to multiple projects and clients (healthcare industry preferred Hands-on experience using Bedrock or OpenAI solution Python or R programming experience B.S. Degree Excellent communication skills with ability to be both solution oriented (customer facing, gathering requirements) as well as hands-on with tech/development team PREFERRED SKILLS: Government/Federal agency experience AWS Big Data Certifications Healthcare industry experience RESPONSIBILITIES: Build and configure end-to-end MLOps pipeline on AWS cloud for model management, model deployment & service and model governance using AWS SageMaker. Use Amazon SageMaker Studio for development and tracking. Implement CI/CD pipelines using GITLAB to automate model deployment and updates, enabling rapid iterations and reducing time-to-market. Create Framework for deploying Client models to production environments using SageMaker endpoints and set up monitoring to track model performance and drift over time. Set up and run SageMaker Clarify bias analysis through Amazon Sagemaker Experiments to check the model for potential biases. Setup SageMaker Model Monitor to allow clients to select data from a menu of options such as prediction output, and capture metadata such as timestamp, model name, and end point so that clients can analyze model predictions based on the metadata. Maintain logs for reproducibility, validation, conformity, and auditability. Add Cohort model explainability used in the deployment phase, specifically in the model validation step before deployment. Implement static deployment strategies (using traffic routing patterns) to deploy Client model(s) - Blue/Green, A/B Keywords: continuous integration continuous deployment machine learning rlang AWS Machine Learning Consultant [email protected] |
| [email protected] View all |
| Thu Jun 27 07:43:00 UTC 2024 |