CDW

Senior Field Solutions Architect, AI/HPC

CDW

full-time

Posted on:

Location Type: Hybrid

Location: Toronto • 🇨🇦 Canada

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Salary

💰 $145,600 - $203,840 per year

Job Level

Senior

Tech Stack

Kubernetes

About the role

  • Lead discovery to translate business outcomes into technical requirements and reference architectures for AI/HPC platforms (GPU clusters, distributed training, model serving, orchestration).
  • Design scalable compute using leading GPU technologies; specify low-latency fabrics (InfiniBand, RoCE) and high-performance storage (e.g., NVMe, parallel file systems).
  • Define security, resilience, data-management, and cost-optimization patterns; document assumptions and trade-offs.
  • Build solution designs, sizing guidance, diagrams, and BOMs with clear scope boundaries and delivery alignment.
  • Support proposals, statements of work, and risk identification; ensure feasibility with delivery leadership.
  • Present technical content to diverse audiences (engineers to executives) and handle deep-dive Q&A.
  • Act as a trusted advisor on workload placement, performance tuning considerations, and modernization paths.
  • Partner with account managers to qualify opportunities and position the right mix of products, professional services, and managed services.
  • Coordinate architecture validation with key vendors; stay current on roadmaps affecting AI/HPC platform choices.
  • Collaborate with professional services and project management so designs are deployable and milestones are clear.
  • Contribute reusable assets (templates, calculators, patterns) and improve architecture quality through peer reviews.
  • Advocate for accurate opportunity data and case hygiene to support forecasting and delivery planning.

Requirements

  • Hands-on presales architecture experience designing AI/HPC infrastructure: GPU-accelerated compute, distributed training, model serving, high-performance storage, and low-latency networking.
  • Strong consulting fundamentals: discovery, requirements translation, executive-ready communication, and decision framing.
  • Proficiency creating architecture diagrams, BOMs, sizing, and cost/comparison analyses.
  • Demonstrated collaboration across delivery, program/project management, managed services, and vendor partners.
  • Ability to simplify complexity, build stakeholder confidence, and drive consensus.
  • Familiarity with orchestration and scheduling (Kubernetes, Slurm, PBS); parallel file systems (Lustre, BeeGFS); object storage; and benchmarking/performance methods.
  • Exposure to MLOps/ModelOps pipelines, inference serving, RAG infrastructure, or data-platform integration.
  • Industry knowledge in research, public sector, or enterprise analytics use cases.
Benefits
  • Health insurance
  • Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
AI infrastructure designHPC infrastructure designGPU-accelerated computedistributed trainingmodel servinghigh-performance storagelow-latency networkingarchitecture diagramscost/comparison analysesMLOps
Soft skills
consulting fundamentalsexecutive-ready communicationdecision framingcollaborationsimplifying complexitybuilding stakeholder confidencedriving consensus