Amazon Sagemaker Platform Admin
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Amazon SageMaker Platform Admin in Atlanta, GA, on a contract basis. Key skills include AWS, Docker, Terraform, and DevOps. Experience in managing Amazon SageMaker environments and implementing security best practices is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 30, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Atlanta, GA
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🧠 - Skills detailed
#Datasets #Security #DevOps #Monitoring #ECR (Elastic Container Registery) #Docker #AWS (Amazon Web Services) #Model Deployment #IAM (Identity and Access Management) #S3 (Amazon Simple Storage Service) #Automation #Deployment #Scala #VPC (Virtual Private Cloud) #SageMaker #Documentation #ML (Machine Learning) #Compliance #Data Science #Terraform #Cloud
Role description
Role: Amazon Sagemaker platform Admin
Location: Atlanta, GA
Contract
Job Summary:
We are looking for an experienced SageMaker Platform Administrator to manage, maintain, and optimize our enterprise-scale Amazon SageMaker environment. In this role, you will be responsible for ensuring the platform’s reliability, security, scalability, and ease of use for data scientists and ML engineers. You will collaborate with DevOps, Security, and Data Science teams to enable seamless development, training, deployment, and monitoring of machine learning models.
Key Responsibilities:
• Administer and maintain the Amazon SageMaker platform, including Studio, Notebooks, Training Jobs, Inference Endpoints, and Pipelines.
• Implement and enforce security best practices, including IAM roles, VPC configuration, encryption, and access controls.
• Automate environment setup, user onboarding, and resource provisioning using CloudFormation, Terraform, or AWS CDK.
• Monitor platform usage, performance, cost, and health using CloudWatch, AWS Cost Explorer, and other monitoring tools.
• Collaborate with data scientists to troubleshoot platform issues, optimize compute usage (e.g., instance selection, spot vs. on-demand), and improve model deployment workflows.
• Maintain and manage custom Docker images, lifecycle configurations, and shared datasets.
• Integrate SageMaker with other services (e.g., ECR, S3, EFS, Secrets Manager, CodePipeline, CloudTrail).
• Maintain compliance with enterprise governance and security policies.
• Drive improvements in platform usability, documentation, automation, and overall ML developer experience.
Role: Amazon Sagemaker platform Admin
Location: Atlanta, GA
Contract
Job Summary:
We are looking for an experienced SageMaker Platform Administrator to manage, maintain, and optimize our enterprise-scale Amazon SageMaker environment. In this role, you will be responsible for ensuring the platform’s reliability, security, scalability, and ease of use for data scientists and ML engineers. You will collaborate with DevOps, Security, and Data Science teams to enable seamless development, training, deployment, and monitoring of machine learning models.
Key Responsibilities:
• Administer and maintain the Amazon SageMaker platform, including Studio, Notebooks, Training Jobs, Inference Endpoints, and Pipelines.
• Implement and enforce security best practices, including IAM roles, VPC configuration, encryption, and access controls.
• Automate environment setup, user onboarding, and resource provisioning using CloudFormation, Terraform, or AWS CDK.
• Monitor platform usage, performance, cost, and health using CloudWatch, AWS Cost Explorer, and other monitoring tools.
• Collaborate with data scientists to troubleshoot platform issues, optimize compute usage (e.g., instance selection, spot vs. on-demand), and improve model deployment workflows.
• Maintain and manage custom Docker images, lifecycle configurations, and shared datasets.
• Integrate SageMaker with other services (e.g., ECR, S3, EFS, Secrets Manager, CodePipeline, CloudTrail).
• Maintain compliance with enterprise governance and security policies.
• Drive improvements in platform usability, documentation, automation, and overall ML developer experience.
