Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Sr Ethernet AI Network Engineer to provide senior network engineering services for Ethernet-based AI data center fabrics supporting GPU compute clusters, storage connectivity, and management plane services. The role focuses on deploying and validating Ethernet fabrics, troubleshooting complex production network issues, supporting distributed training workloads, executing network changes, and developing operational standards for fabric health.
Responsibilities
- Deploy, validate, and support Ethernet fabrics used for AI and HPC cluster environments
- Troubleshoot Layer 1 through Layer 4 issues involving optics, transceivers, cabling, link bring up, VLANs, MLAG, ECMP, BGP, underlay and overlay reachability, congestion, and packet loss
- Validate network readiness for distributed training workloads and large east west traffic patterns
- Diagnose performance issues related to buffer pressure, microbursts, PFC behavior, Quality of Service policy, MTU mismatches, routing instability, and oversubscription
- Partner with Linux, storage, and cluster deployment teams to isolate host versus network fault domains
- Review and execute change plans for switch provisioning, firmware upgrades, topology expansion, and maintenance activities
- Capture packet level and counter based evidence to support root cause analysis
- Develop operational standards for cable mapping, port policy consistency, and fabric health validation
Skills
- 7 or more years of experience in large scale data center networking, including high bandwidth Ethernet fabrics
- Strong experience with spine leaf architectures, routing, switching, and production troubleshooting
- Hands on experience with BGP, EVPN, VXLAN, MLAG, ECMP, Quality of Service, Priority Flow Control, RoCE considerations, and telemetry analysis
- Experience validating optics, breakout configurations, cable plant integrity, and port level consistency
- Proven ability to troubleshoot distributed application impacts caused by network behavior
- Experience using switch command line interfaces, automation tools, and packet and counter analysis workflows
- Strong documentation skills for topology diagrams, incident timelines, and remediation planning
- Direct experience supporting AI fabrics carrying large scale GPU collective traffic
- Familiarity with SONiC, Cumulus Linux, or similar network operating systems used in AI data centers
- Experience with streaming telemetry, Prometheus, Grafana, and network site reliability engineering operating models
Benefits
- 12 month remote contract opportunity within the United States
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