Senior Network Engineer - Operations
Job Description
Key Skills Required
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We’re looking for a Senior Network Engineer - Operations who is responsible for the day-to-day operation, maintenance, automation, and on-call support of large-scale data center networks supporting AI and GPU workloads. Scripting and tooling skills are a differentiator to reduce toil and speed incident resolution.
What You’ll Do
- Operate and maintain large Ethernet fabrics
- Participate in on-call rotation and incident response
- Execute maintenance, upgrades, and hardware replacements
- Troubleshoot latency, packet loss, and connectivity issues
- Support continuous scale-out growth of production networks
- Build or extend automation/tooling to streamline operations (Python/Go, Ansible, Git-backed workflows)
Who You Are
Required Qualifications
- 5+ years data center network operations experience
- Multi-Vendor & NOS Experience - Vendors: Juniper, Cisco, Arista, Whitebox, NOS: Junos, IOS/IOS-XE, NX-OS, EOS, SONiC
- Hands-on experience with large Ethernet fabrics and edge networks
- Strong understanding of scale-up vs scale-out architectures
- Experience operating production networks at scale
Preferred Qualifications
- 100G+ environments
- Familiarity with optical standards and transceiver types (e.g., 100G/400G/800G, SR/LR/ER, DWDM)
- AI, GPU, or HPC exposure
- Automation/scripting to reduce operational toil (Python/Go/Ansible), network APIs/SDKs, CI/CD for network changes, telemetry/log parsing for rapid incident triage
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TensorWave
View Company ProfileTensorWave is a premier, enterprise-grade cloud infrastructure provider engineered to orchestrate massive-scale artificial intelligence (AI) and high-performance computing (HPC) workloads. Operating as a high-velocity digital ecosystem, the company eliminates the operational friction and supply chain bottlenecks of legacy hyperscalers by exclusively deploying advanced AMD Instinct™ accelerators—including the MI300X and MI325X—across highly optimized bare-metal environments. Moving beyond the rigid memory constraints of traditional GPU cloud models, TensorWave provides industry-leading capacity with up to 288GB of HBM3e per accelerator, directly tackling the immense requirements of next-generation Large Language Models (LLMs) and complex machine learning training clusters. Under the hood, their UEC-ready networking architecture and direct liquid cooling systems seamlessly integrate into existing AI pipelines, ensuring ultra-low latency inference and blistering training speeds. What sets TensorWave apart is its uncompromising dedication to open-source democratization and performance accessibility; by bridging the gap between top-tier compute resources and massive cost-efficiency, the platform empowers scaling enterprises to radically accelerate AI innovation without the burden of building internal hardware infrastructure.
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