Consulting Engineer – AI Networking & Fabric Architecture

CDW

19d•$77,250 - $108,265

About The Position

The AI Infrastructure Consulting Engineer – AI Networking & Fabric Architecture implements, validates, and optimizes high-performance network fabrics for accelerated computing and distributed AI workloads. This client-facing, billable role delivers high-quality work and ensures solutions are implemented as designed to the client’s satisfaction and approval. The engineer translates workload, topology, performance, and operational requirements into reliable network configurations for GPU clusters and adjacent data-center infrastructure. The role includes managing time and expenses against the Statement of Work (SOW), escalating potential scope, schedule, or budget issues, and providing technical input for SOWs, ROMs, LOEs, implementation plans, and solution validation when requested. The engineer creates and maintains project documentation, as-built configurations, test plans, work logs, and client handoff materials, and transfers technical knowledge to clients. The position may require work beyond scheduled shifts, select weekends, or overtime, as well as travel to CDW locations or client sites. The stated pay range is $77,250–$108,265, depending on experience and skill set, with an annual bonus target of 10% subject to plan terms and conditions. Salary ranges may be subject to geographic differentials.

Requirements

Bachelor’s degree and 3 years of experience delivering complex network or data-center infrastructure solutions, including planning, implementation, integration, and support; alternatively, 7 years of such experience.
Hands-on experience implementing and troubleshooting enterprise data-center networks.
Working knowledge of Ethernet switching, routing, VLANs, LAG/MLAG, BGP, cabling, optics, telemetry, and network validation.
Understanding of lossless fabric concepts, including priority flow control, explicit congestion notification, congestion management, buffering, and quality of service.
Experience with InfiniBand, RoCE, RDMA, GPUDirect RDMA, or comparable high-performance networking technologies.
Ability to interpret network designs, port maps, bills of materials, cable matrices, and implementation plans.
Ability to build and execute test plans aligned to project deliverables and the Statement of Work.
Strong written and verbal communication skills, including the ability to interact effectively with leadership stakeholders and clients.
Ability to create accurate as-built documentation and transfer technical knowledge to clients.
Attention to detail, analytical problem-solving skills, and ability to balance competing priorities while meeting deadlines.
Ability and willingness to work beyond regularly scheduled shifts, select weekends, or overtime as needed to meet operational needs.
Ability and willingness to travel as needed to CDW locations or client sites.
Valid U.S. driver’s license.

Nice To Haves

Experience with NVIDIA Spectrum-X, Spectrum Ethernet, Quantum InfiniBand, Cumulus Linux, SONiC, or comparable data-center networking platforms.
Experience with NVIDIA UFM, NetQ, telemetry, automation, benchmarking, or fabric-management tooling.
Familiarity with rail-optimized topologies, distributed training communication patterns, and AI storage-network requirements.
Preferred NVIDIA certifications include NVIDIA-Certified Professional AI Networking (NCP-AIN) and NVIDIA-Certified Professional AI Infrastructure (NCP-AII).

Responsibilities

Manage time and expenses to meet or exceed expectations defined in the Statement of Work, and proactively escalate potential scope, schedule, or budget issues.
Implement and validate high-performance AI network fabrics using InfiniBand, Ethernet, RoCE, or related technologies.
Configure and troubleshoot lossless networking, congestion management, quality of service, routing, telemetry, cabling, optics, and fabric-management components.
Support GPU-to-GPU, node-to-node, storage, management, and client network connectivity for distributed training and inference environments.
Execute installation, configuration, burn-in, performance validation, troubleshooting, documentation, and knowledge transfer in customer environments.
Provide technical input for SOWs, ROMs, LOEs, implementation plans, and solution validation when requested.
Create and maintain project documentation, as-built configurations, test plans, work logs, and client handoff materials.
Transfer technical knowledge to clients.