Company:

QT Technologies Ireland Limited

Job Area:

Information Technology Group, Information Technology Group > IT Engineering
General Summary:

• GPU troubleshooting and incident response: Diagnose and resolve complex issues regarding dependencies between GPU drivers and software, Nvidia toolkit errors, or GPU component failure.

• Run:AI Platform Support: Provide expert support for the Run:AI platform, assisting customers with the deployment, configuration, and management of Kubernetes clusters that handle AI/ML workloads. This includes setting up the platform, configuring resource pools (GPU, CPU), and optimizing Kubernetes namespaces to ensure proper orchestration of workloads.

• Workload Optimization on Kubernetes: Assist customers in optimizing dynamic resource allocation for their AI/ML workloads by utilizing the Run:AI scheduler in conjunction with Kubernetes's native tools. Help manage job preemption, scheduling priorities, and horizontal scaling of workloads across clusters.

• Kubernetes Troubleshooting & Incident Response: Diagnose and resolve complex issues related to Kubernetes cluster management, including pod failures, node connectivity issues, and namespace misconfigurations. Provide support in handling incidents such as job contention, GPU misallocation, and failed containerized workloads, ensuring smooth operation across the entire Kubernetes environment.

• Integration Support: Help customers integrate Run:AI into their existing Kubernetes-based ML infrastructure. Ensure seamless operation of AI/ML pipelines, covering data flow, distributed training, and model deployment. Troubleshoot issues arising from the interaction between Run:AI, Kubernetes, and other ML tools (e.g., Tensor Flow, Py Torch, Kubeflow).

• Security and Best Practices in Kubernetes: Advise customers on security best practices for Kubernetes clusters handling sensitive ML workloads, such as secure pod communications, role-based access control (RBAC), and resource isolation for multi-tenant clusters. Ensure Kubernetes and containerized environments are secure and compliant with organizational policies.

• Collaboration with HQ: Work closely with the engineering and product teams in HQ, providing feedback on Kubernetes-related issues, cluster optimization features, and improvements to the Run:AI platform. Escalate complex issues and contribute to ongoing platform development.

• Training & Documentation: Develop training materials and deliver technical workshops on using Run:AI in Kubernetes environments. Maintain up-to-date documentation on best practices for configuring and managing Kubernetes clusters for AI/ML workloads, focusing on high availability, performance, and security.

Minimum Qualifications:

• 4+ years of IT-related work experience with a Bachelor's degree.

OR
7+ years of IT-related work experience without a Bachelor’s degree.

Physical Requirements:

• Frequently transports and installs equipment up to 20 lbs.

Requirements

• 3+ years of experience in technical support roles with strong expertise in Kubernetes administration, container orchestration, and AI/ML workload management.

• 1+ year of general GPU administration, addressing issues with driver conflicts, hardware failures, and performance issues

• In-depth knowledge of Kubernetes (CKA or CKAD certification highly preferred), including core components like Kubelet, Kube-API, Kube-scheduler, and etc.

• Experience with configuration management tools (Puppet, Chef, Ansible) and Kubernetes management platforms like Rancher a plus

• References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

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