AI Network Engineer

Description

Overview:

We are seeking an experienced AI Network Engineer to support and optimize high-performance infrastructure powering AI/ML workloads. This role focuses on designing and maintaining GPU-accelerated environments leveraging NVIDIA technologies, high-throughput networking, and low-latency architectures.

Key Responsibilities:

+ Design, implement, and support high-performance networks for AI/ML workloads, including GPU clusters and distributed training environments

+ Deploy and optimize NVIDIA-based infrastructure (DGX systems, HGX platforms, or GPU clusters)

+ Configure and manage high-speed networking technologies such as Infini Band, RoCE, and 100/200/400

Gb Ethernet

+ Optimize network performance for east-west traffic, low latency, and large data throughput required for AI model training

+ Integrate NVIDIA software stack (CUDA, NCCL, GPU Cloud, AI Enterprise) with networking and compute environments

+ Troubleshoot performance bottlenecks across network, storage, and GPU interconnects

+ Collaborate with AI/ML engineers to ensure infrastructure meets training and inference demands

+ Support automation and infrastructure-as-code initiatives for scalable AI environments

Requirements

Required Qualifications:

+ 5+ years of experience in network engineering or infrastructure engineering

+ Hands-on experience with high-performance networking (Infini Band, RDMA, RoCE)

+ Experience supporting GPU-based or HPC environments

+ Strong knowledge of data center networking (L2/L3, BGP, EVPN, VXLAN)

+ Familiarity with Linux systems and performance tuning

+

Experience with NVIDIA ecosystems (DGX, CUDA, NCCL, or similar)

+ Ability to diagnose low-latency and high-throughput network issues

Preferred Qualifications:

+

Experience with NVIDIA AI Enterprise or DGX Super

POD environments

+ Knowledge of AI/ML workflows and distributed training frameworks (PyTorch, Tensor Flow)

+ Familiarity with Kubernetes for AI workloads

+

Experience with storage solutions supporting AI workloads (parallel file systems, NVMe over Fabrics)

+ Exposure to cloud-based GPU environments (AWS, Azure, GCP)

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