Amazon SageMaker HyperPod Engineers

About the Role

The Amazon SageMaker HyperPod Engineer is a full-time, on-site role based in Hyderabad. This role involves designing, deploying, and managing SageMaker HyperPod environments to support large-scale AI/ML workflows for scientific and research workloads.

Key Responsibilities

• Configuring clusters.
• Optimizing training and inference pipelines.
• Monitoring performance.
• Implementing security and compliance best practices in AWS.
• Collaborating with data scientists, ML engineers, and DevOps teams to integrate HyperPod solutions with existing HPC and data platforms.
• Troubleshooting issues.
• Improving reliability and scalability.
• Documenting architectures.
• Creating automation scripts.
• Contributing to best practices and reusable patterns for future projects.

Requirements & Tech Stack

• Strong AWS skills, including Amazon SageMaker (HyperPod), EC2, S3, networking basics, and IAM security controls.
• Experience with machine learning workflows, such as model training, distributed training, experiment tracking, and MLOps practices.
• Infrastructure and automation skills, including Infrastructure as Code (e.g., AWS CloudFormation, Terraform), scripting (Python or Bash), and CI/CD pipelines.
• Fundamental Linux administration skills, including system monitoring, resource management, and troubleshooting in HPC or cloud environments.
• Strong problem-solving skills.
• Ability to collaborate in cross-functional teams.
• Clear written and verbal communication abilities.
• Relevant experience with high-performance computing, big data platforms, or large-scale AI/ML frameworks is highly beneficial.

Qualifications

• Bachelor’s or higher degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.