Product Manager - AI Data Center Infrastructure

• Define and own product strategy and requirements for next-generation AI data center networking platforms supporting large-scale GPU clusters.
• Drive architecture for scale-up and scale-out AI fabrics delivering deterministic performance, ultra-low latency, and high bandwidth efficiency.
• Translate AI workload characteristics into clear product roadmaps for switching, routing, automation, and telemetry capabilities.
• Specify Ethernet fabric designs using Juniper QFX platforms and Apstra-based intent-driven automation.
• Lead requirements for GPU, NIC, and interconnect technologies aligned with NVIDIA and AMD ecosystems.
• Define interoperability requirements across switches, NICs, DAC, AEC, ACC, and optical transceivers.
• Specify L2/L3 architectures including EVPN-VXLAN, Class-E IPv4, and AI-optimized buffer and congestion management.
• Drive performance optimization by analyzing GPU job behavior to identify congestion, packet loss, and microbursts.
• Define and tune ECN, RDMA/ROCEv2, PFC, and traffic engineering policies for AI training and inference workloads.
• Lead validation, scale testing, and certification of new switch software, optics, NIC firmware, and GPU platforms.
• Participate in root-cause analysis for link stability, FEC/PCS errors, and power or thermal-related performance issues.
• Work independently while driving alignment across engineering, operations, platform teams, and strategic ecosystem partners.

Required Qualifications

• 510+ years of experience in data center networking, AI infrastructure, or HPC environments.
• Strong hands-on experience with Juniper QFX platforms and JunOS.
• Deep understanding of GPU architectures including NVIDIA H100/H200, GB200/GB300, NVLink/NVSwitch, and AMD MI300/MI400 with Infinity Fabric.
• Proven expertise in scale-up GPU interconnects and scale-out Ethernet fabrics.
• Strong knowledge of RDMA/ROCEv2, ECN, PFC, buffer management, and congestion control.
• Hands-on experience troubleshooting high-speed optics, AEC/ACC cables, link training, and NIC firmware.
• Proficiency in automation and scripting using Python, Ansible, Bash, and Terraform.
• Familiarity with distributed AI workloads and collective communication libraries such as NCCL and RCCL.

Preferred Qualifications

• Industry certifications such as JNCIE, CCIE, NCP-AII, NCA-AIIO, NCP-AIO, or NCP-AIN.
• Experience with Apstra or other intent-based networking platforms.
• Knowledge of 1.6T optics, 200G PAM4 SerDes, and CPO/LPO architectures.
• Experience supporting liquid-cooled GPU clusters and rack-level power and network design.
• Understanding of data center operations, observability, and SLA-driven AI infrastructure environments.