As an AI/ML Infrastructure Engineer on the GPU Strategic Customers Engineering team, you will play a critical role in designing, implementing, and maintaining the infrastructure that supports our AI and machine learning initiatives. You will work closely with data scientists, software engineers, and IT professionals to ensure that our AI/ML models are deployed efficiently, securely, and at scale. Your expertise will be crucial in optimizing our infrastructure for performance, reliability, and cost-effectiveness.

Qualifications:

• Experience in scripting and automation using tools like Ansible, Terraform, and/or Kubernetes.
• Experience with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools for managing distributed systems.
• Solid understanding of networking concepts, security principles, and best practices.  
• Excellent problem-solving skills, with the ability to troubleshoot complex issues and drive resolution in a fast-paced environment.
• Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.
• Strong documentation skills with experience documenting infrastructure designs, configurations, procedures, and troubleshooting steps to facilitate knowledge sharing, ensure maintainability, and enhance team collaboration.
• Strong Linux skills with hands-on experience in Oracle Linux/RHEL/CentOS, Ubuntu, and Debian distributions, including system administration, package management, shell scripting, and performance optimization.

Preferred Qualifications

• Strong proficiency in at least one of the programming languages such as Python, Rust, Go, Java, or Scala
• Proven experience designing, implementing, and managing infrastructure for AI/ML or HPC workloads.
• Understanding machine learning frameworks and libraries such as TensorFlow, PyTorch, or sci-kit-learn and their deployment in production environments is a plus. 
• Familiarity with DevOps practices and tools for continuous integration, deployment, and monitoring (e.g., Jenkins, GitLab CI/CD, Prometheus).
• Strong experience with High-Performance Computing systems 

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