The Next Chapter

• Tuning the performance of clusters and InfiniBand networks to ensure optimal operation in HPC and GPU-based environments. • Analyzing and troubleshooting the root cause of issues related to GPUs and InfiniBand networks, and proposing corrective actions. • Integrating new hardware into the existing infrastructure, including support for new GPU hardware through software stacks like Kubernetes, QEMU, and KVM. • Enhancing automation systems for proactive monitoring, detecting, and resolving issues in GPU and InfiniBand environments. • Configuring and managing GPU devices and InfiniBand fabrics, ensuring efficient and reliable operation.

• Trace, profile, tune and optimize Linux kernel & subsystems (CPU scheduling, memory management, networking stack) for GPU clusters and InfiniBand fabrics • Troubleshoot and resolve complex performance bottlenecks • Integrate and validate new GPU hardware & infra (KVM/QEMU, PCIe devices, Kubernetes) • Improve monitoring, alerting, and automation for large-scale, distributed systems • Occasionally assist customers in optimizing workloads

• Join a fast-moving AI infrastructure team working on large-scale ML workloads • Design and validate production-grade distributed training and large-scale inference architectures on large GPU clusters • Work hands-on with customers to debug, optimize, and scale ML workloads across multi-node GPU environments • Act as a technical authority on GPU performance, networking, and schedulers • Collaborate closely with engineering, product, and R&D to influence roadmap decisions