Lead technical discovery and solution positioning for enterprise AI infrastructure opportunities, translating business outcomes into reference architectures and value propositions.
Own pre-sales deliverables including architectures, diagrams, sizing, BOMs, proposals.
Deliver executive and technical presentations focused on NVIDIA AI Enterprise (NVAIE), LLM training/inference, and accelerated analytics.
Guide clients through technology selection, roadmap development, and business case creation for large-scale AI initiatives.
Architect end-to-end AI platforms using NVIDIA DGX/HGX, Blackwell (B100/B200), Hopper (H100/H200), Grace/Grace-Hopper (GH200), L40S, NVLink/NVSwitch, InfiniBand (NVIDIA Quantum), RoCE, and DPU offload patterns.
Design solutions leveraging AMD Instinct (MI300/MI300X) as appropriate, articulating trade-offs in CPU/GPU/DPU, interconnect topology, and cluster scale-out.
Integrate NVIDIA AI Enterprise components (CUDA, cuDNN, TensorRT, Triton Inference Server, RAPIDS) and common ML frameworks (PyTorch, TensorFlow) with orchestration platforms.
Experience integrating on-prem GPU clusters with cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI) for hybrid bursting and workload mobility.
Advise on MLOps platforms (MLflow, Kubeflow, Weights & Biases), CI/CD, and governance for multi-tenant AI environments.
Build and maintain relationships with NVIDIA, AMD, Run:AI, OEMs, and networking vendors, aligning campaigns with partner programs and incentives.
Contribute feedback to vendor engineering and product teams, coordinating joint enablement and reference designs.
Create repeatable assets such as validated designs, sizing calculators, POV guides, deployment runbooks, and competitive playbooks.
Mentor SEs and delivery consultants, leading internal training on AI scheduling, performance tuning, and operational best practices.
Lead proof-of-value (POV) and proof-of-concept (POC) engagements, including success criteria, benchmarking, and recommendations.

Requirements

Proven experience architecting and deploying NVIDIA GPU-based AI platforms (NVAIE, DGX/HGX, Blackwell, Hopper, Grace, L40S, H100/H200, B100/B200, GH200) and/or AMD Instinct MI300/MI300X.
Experience with Run:AI, NVIDIA Base Command, Kubernetes (GPU Operator), Slurm, and/or vSphere with Tanzu for AI/ML workloads.
Advanced knowledge of AI/ML frameworks and libraries (PyTorch, TensorFlow, RAPIDS, Triton, CUDA, cuDNN, TensorRT).
Strong understanding of high-speed networking for AI (InfiniBand, RoCE, DPU integration, NVLink, NVSwitch).
Experience integrating on-prem AI infrastructure with public cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI) and hybrid architectures.
Experience leading pre-sales campaigns, POV/POC management, and executive presentations.
Ability to identify and leverage emerging datacenter and AI technologies to drive innovative solutions.
Strong analytical skills for troubleshooting complex environments, including storage, compute, networking, and AI workloads.
Skilled at guiding clients through decision-making with clear, strategic recommendations.
Proven track record of working effectively across sales, engineering, and vendor teams.
Knowledge of datacenter security best practices and regulatory compliance.

Benefits

Medical, Dental, and Vision Insurance
401(k)
Paid company holidays
Paid time off
Paid parental and caregiver leave
Plus more! See benefits https://www.aheadbenefits.com/ for additional details.

Applicant Tracking System Keywords

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

Hard Skills & Tools

NVIDIA AI EnterpriseNVIDIA DGXNVIDIA HGXAMD InstinctPyTorchTensorFlowCUDAcuDNNTensorRTMLOps

Soft Skills

analytical skillsstrategic recommendationsmentoringrelationship buildingcommunicationleadershiptroubleshootingguiding clientscollaborationpresentation skills