AWS Infrastructure Specialist

**This is a temporary contract.

**Must be located within commuting distance from Troy MI, or Hicksville NY to work a hybrid schedule.

We are seeking a skilled AWS Infrastructure & Development Specialist with extensive experience in AWS services, and focusing on SageMaker and Data Wrangler. The ideal candidate will have between 5-10 years of experience in cloud infrastructure and development, with a strong technical background in managing and optimizing AWS tools for large-scale data operations.

In this role, you will be responsible for operationalizing the SageMaker platform and providing end-to-end support for various client and internal requests. You will play a key role in the onboarding process, ensuring that users are seamlessly integrated with the platform, and guide them through critical processes such as data preparation, routine maintenance, and capacity planning/monitoring.

Key Responsibilities:

• AWS SageMaker & Data Wrangler Support: Operationalize SageMaker for users, providing assistance with onboarding, data preparation, and supporting ongoing requests.
• Routine Maintenance: Monitor and maintain the platform to ensure optimal performance, scalability, and reliability.
• Capacity Planning & Monitoring: Proactively manage and monitor platform capacity, making recommendations for scaling as needed.
• Chargeback Modeling & Reporting: Develop and implement chargeback models to optimize resource usage and provide financial transparency.
• Best Practices & Security: Ensure that the platform adheres to AWS best practices in terms of security, scalability, and cost management.
• General AWS Infrastructure Support: As needed, assist with AWS infrastructure tasks such as network configuration, resource provisioning, and troubleshooting.

Skills & Qualifications:

• 5-10 years of experience with AWS infrastructure and development.
• Strong expertise with AWS SageMaker and Data Wrangler for machine learning workflows.
• Python programming skills for supporting SageMaker applications.
• Familiarity with Data Wrangler’s notebook-based interface for exploratory data analysis, built-in visualizations, and common data cleaning operations