<div>Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.</div><div> </div>

Nava

About Nava&nbsp;&nbsp;
Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.&nbsp;&nbsp;
About the Role&nbsp;
The Network Deployment team leads engineering and commissioning of high-performance networks across Nava's GPU clusters, including GPU fabrics, Ethernet leaf-spine, storage and out-of-band management networks. This is a hands-on leadership team where the fabric is treated as code.&nbsp;
Lead deployment and commissioning of high-performance GPU cluster networks, including InfiniBand, RoCE, Ethernet fabrics, and management networks. Own network automation, integration, performance validation, troubleshooting, and production readiness.&nbsp;
What You’ll Do&nbsp;
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Lead installation, configuration and production acceptance of InfiniBand, RoCE and high-speed Ethernet fabrics.&nbsp;
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Own topology implementation, switch configuration, cable/optics validation, routing and management connectivity.&nbsp;
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Validate RDMA fabric behavior, bandwidth, latency, congestion and distributed GPU communications.&nbsp;
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Build automated configuration, discovery, preflight and regression tests for repeatable multi-site network commissioning.&nbsp;
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Resolve cross-domain integration defects with compute/storage engineers and network OEMs.&nbsp;
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Manage network deployment engineers, technical milestones, standards, documentation and production handover.&nbsp;
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Tune PFC watermarks and ECN thresholds to keep RDMA lossless under all-reduce incast; validate rail-optimized topologies and adaptive routing on the GPU backend fabric.&nbsp;
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Define acceptance gates against measurable targets — NCCL all-reduce bus bandwidth within target % of line rate and near-zero PFC pause — using UFM (InfiniBand) and NetQ / Spectrum-X telemetry, plus GPUDirect RDMA and BlueField DPU offload validation.&nbsp;
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What You’ll Need&nbsp;
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10–15 years in data center network engineering including leading network deployment teams.&nbsp;
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Advanced BGP, EVPN/VXLAN, ECMP, leaf-spine architecture and Linux network troubleshooting.&nbsp;
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Strong understanding of InfiniBand and/or RoCEv2, RDMA, PFC/ECN and fabric congestion.&nbsp;
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Hands-on high-speed switches (100/200/400G+) and Python/Ansible or equivalent network automation.&nbsp;
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Experience with network validation and root-cause analysis at hardware and protocol layers.&nbsp;
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Within that, hands-on GPU / AI-datacenter exposure is what we weigh most — we value demonstrated depth over years, as the field is still emerging.&nbsp;
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<li>NVIDIA networking, NCCL/fabric benchmarking and large AI/HPC cluster builds.&nbsp;</li>
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