Sr Ethernet AI Network Engineer

As a Senior Ethernet AI Network Engineer, you will help design, deploy, and maintain high-performance Ethernet fabrics that power advanced AI and high-performance computing (HPC) environments. You will ensure network infrastructure can support demanding distributed training workloads, high-bandwidth data transfers, and large-scale GPU cluster communications. Working across the full network stack, you will troubleshoot complex connectivity and performance issues, optimize traffic behavior, and validate the reliability of critical data center infrastructure. You will collaborate closely with Linux, storage, and cluster deployment teams to identify root causes and maintain seamless operations. This remote opportunity is suited to an experienced networking professional who enjoys solving complex infrastructure challenges and building scalable operational standards for next-generation AI environments.

• At least 7 years of experience in large-scale data center networking, including high-bandwidth Ethernet fabrics.

• Strong expertise in spine-leaf network architecture, routing, switching, and production network troubleshooting.

• Hands-on experience with BGP, EVPN/VXLAN, MLAG, ECMP, QoS, PFC, and RoCE-related networking considerations.

• Ability to interpret network telemetry and diagnose complex connectivity, congestion, and performance issues.

• Experience validating optical transceivers, breakout configurations, cabling infrastructure, and port-level configuration consistency.

• Demonstrated ability to troubleshoot distributed application performance issues caused by network behavior.

• Practical experience using switch command-line interfaces, network automation tools, and packet and counter analysis workflows.

• Strong documentation skills, including maintaining network topology records, incident timelines, technical findings, and remediation plans.

• Excellent analytical and problem-solving abilities, with the capacity to investigate complex issues across multiple network layers.

• Strong communication and collaboration skills, particularly when working with infrastructure, Linux, storage, and cluster engineering teams.

• Direct experience with AI networking fabrics supporting large-scale GPU collective traffic is preferred.

• Familiarity with SONiC, Cumulus Linux, or other network operating systems used in AI data center environments is an advantage.

• Experience with streaming telemetry, Prometheus, Grafana, and network Site Reliability Engineering (SRE) operating models is preferred.