AI/ML Infrastructure Engineer

. 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

• Take ownership of problems and work to identify solutions.
• Ability to think through the solution and identify/document potential issues impacting your customers.
• Design, deploy, and manage infrastructure components such as cloud resources, distributed computing systems, and data storage solutions to support AI/ML workflows.
• Collaborate with scientists and software/infrastructure engineers to understand infrastructure requirements for training, testing, and deploying machine learning models.
• Implement automation solutions for provisioning, configuring, and monitoring AI/ML infrastructure to streamline operations and enhance productivity.
• Optimize infrastructure performance by tuning parameters, optimizing resource utilization, and implementing caching and data pre-processing techniques.
• Ensure security and compliance standards are met throughout the AI/ML infrastructure stack, including data encryption, access control, and vulnerability management.
• Troubleshoot infrastructure performance, scalability, and reliability issues and implement solutions to mitigate risks and minimize downtime.
• Stay updated on emerging technologies and best practices in AI/ML infrastructure and evaluate their potential impact on our systems and workflows.
• Document infrastructure designs, configurations, and procedures to facilitate knowledge sharing and ensure maintainability.