Senior Ethernet AI Network
VDart, Inc.
Los Angeles, CA, United States
14 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
7 years minimum
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Border Gateway Protocol
Big Data
Network Congestion
Data Centers
Linux
Ethernet
Firmware
Network Architecture
Routing
Prometheus
Virtual Local Area Networks
+3 more
Graphics Processing Unit (GPU)
Computer Network Operations
Grafana
Job description
- Troubleshoot L1 through L4 issues involving optics, transceivers, cabling, link bring-up, VLANs, MLAG, ECMP, BGP, underlay/overlay reachability, congestion, and packet loss.
- Validate network readiness for distributed training and large east-west traffic patterns.
- Diagnose performance issues related to buffer pressure, microbursts, PFC behavior, QoS policy, MTU mismatch, routing instability, and oversubscription.
- Work closely 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 events.
- Capture packet-level and counter-based evidence to drive root cause analysis.
- Build operational standards for cable mapping, port policy consistency, and fabric health validation.
Requirements
Ethernet, Artificial Intelligence (AI), Identify Issues, Network Architecture/Engineering, Network Operations Center, Artificial Intelligence (AI), GPU (Graphics Processing Unit), Ethernet, BGP, VLAN (Virtual Local Area Network), * 7+ years in large-scale data center networking, including high-bandwidth Ethernet fabrics.
- Strong experience with spine-leaf design, routing, switching, and production troubleshooting.
- Hands-on skill with BGP, EVPN/VXLAN, MLAG, ECMP, QoS, PFC, RoCE considerations, and telemetry interpretation.
- Experience validating optics, breakout configurations, cable plant integrity, and port-level consistency.
- Proven ability to troubleshoot distributed application impact caused by network behavior.
- Comfort using switch CLI, automation tooling, and packet/counter analysis workflows.
- Strong documentation habits for topology, incident timelines, and remediation plans.
PREFERRED EXPERIENCE
- Direct experience with AI fabrics carrying large-scale GPU collective traffic.
- Familiarity with SONiC, Cumulus Linux, or vendor NOS platforms used in AI data centers.
- Experience with streaming telemetry, Prometheus/Grafana, and network SRE operating models
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