Responsibilities:
1. Take ownership of problems and work to identify solutions.
2. Ability to think through the solution and identify/document potential issues impacting your customers.
3. Design, deploy, and manage infrastructure components such as cloud resources, distributed computing systems, and data storage solutions to support AI/ML workflows.
4. Collaborate with scientists and software/infrastructure engineers to understand infrastructure requirements for training, testing, and deploying machine learning models.
5. Implement automation solutions for provisioning, configuring, and monitoring AI/ML infrastructure to streamline operations and enhance productivity.
6. Optimize infrastructure performance by tuning parameters, optimizing resource utilization, and implementing caching and data pre-processing techniques.
7. Ensure security and compliance standards are met throughout the AI/ML infrastructure stack, including data encryption, access control, and vulnerability management.
8. Troubleshoot infrastructure performance, scalability, and reliability issues and implement solutions to mitigate risks and minimize downtime.
9. Stay updated on emerging technologies and best practices in AI/ML infrastructure and evaluate their potential impact on our systems and workflows.
10. Document infrastructure designs, configurations, and procedures to facilitate knowledge sharing and ensure maintainability.
Qualifications:
11. Experience in scripting and automation using tools like Ansible, Terraform, and/or Kubernetes.
12. Experience with containerization technologies (., Docker, Kubernetes) and orchestration tools for managing distributed systems.
13. Solid understanding of networking concepts, security principles, and best practices.
14. Excellent problem-solving skills, with the ability to troubleshoot complex issues and drive resolution in a fast-paced environment.
15. Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.
16. Strong documentation skills with experience documenting infrastructure designs, configurations, procedures, and troubleshooting steps to facilitate knowledge sharing, ensure maintainability, and enhance team collaboration.
17. 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
18. Strong proficiency in at least one of the programming languages such as Python, Rust, Go, Java, or Scala
19. Proven experience designing, implementing, and managing infrastructure for AI/ML or HPC workloads.
20. Understanding machine learning frameworks and libraries such as TensorFlow, PyTorch, or sci-kit-learn and their deployment in production environments is a plus.
21. Familiarity with DevOps practices and tools for continuous integration, deployment, and monitoring (., Jenkins, GitLab CI/CD, Prometheus).
22. Strong experience with High-Performance Computing systems
Career Level - IC4