Sr Ethernet AI Network Engineer

Accountabilities:: Deploy, validate, and support Ethernet network fabrics for AI and high-performance computing (HPC) cluster environments. Troubleshoot network issues across Layers 1 through 4, including optics, transceivers, cabling, link initialization, VLANs, MLAG, ECMP, BGP, underlay and overlay reachability, congestion, and packet loss. Validate network readiness for distributed AI training and high-volume east-west traffic patterns. Diagnose and resolve network performance issues involving buffer pressure, microbursts, Priority Flow Control (PFC), Quality of Service (QoS) policies, MTU mismatches, routing instability, and oversubscription. Work closely with Linux, storage, and cluster deployment teams to isolate host-related issues from network infrastructure problems. Review and execute network change plans covering switch provisioning, firmware upgrades, topology expansions, and scheduled maintenance activities. Collect and analyze packet-level information, network counters, and telemetry data to support troubleshooting and root-cause investigations. Establish and maintain operational standards for cable mapping, port configuration consistency, and network fabric health validation. Document network incidents, topology changes, troubleshooting findings, and remediation plans to support operational reliability and continuous improvement. Requirements: 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. Benefits: Remote working arrangement. Opportunity to contribute to advanced AI and high-performance computing infrastructure. Exposure to large-scale, high-bandwidth Ethernet environments and modern data center technologies. Opportunities to deepen expertise in AI networking, GPU cluster infrastructure, and network automation. Collaborative environment that encourages ownership, continuous learning, and professional growth. Opportunity to take on meaningful technical challenges and contribute to the development of next-generation network infrastructure.