AWS Machine Learning Consultant

REMOTE AA   Government - Civil Service Posted: 3 weeks ago  

Job Description:
Our large government integrator partner is hiring a key resource in preparation for a large new program to support Department of Health and Human Services (HHS). This role will support a critical mission for their customers in bringing AWS Machine Learning Operations and model deployment practices across multiple projects/programs.

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 solutions
• 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

Nice to have:
• Government/Federal agency experience
• AWS Big Data Certifications

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

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