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Role Description

The AHEAD Senior Modern Datacenter Specialist Solutions Engineer (SSE) is a presales technical leader focused on designing, positioning, and enabling enterprise-scale AI infrastructure solutions. This role owns the technical sales motion from discovery through architecture, demonstration, and business case, across the full AI stack: GPU platforms (NVIDIA & AMD), interconnects, orchestration/scheduling, and AI/ML frameworks. The SSE partners closely with Client Directors, Practice Leaders, and strategic vendors to develop market strategies, drive pipeline, and win campaigns in the US enterprise market.

• 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.

Qualifications

• 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.

Requirements

• Bachelor’s degree in computer science, Engineering, or related field; advanced degree a plus.
• 7+ years in datacenter or cloud infrastructure, with 3+ years directly on AI/GPU platforms and orchestration.

Certifications (preferred)

• NVIDIA Certified Systems Engineer / AI Practitioner; NVAIE enablement badges
• Kubernetes CKA/CKS; GPU Operator experience
• AWS/Azure/GCP Architect; relevant AI/ML specialty

Benefits

• Medical, Dental, and Vision Insurance
• 401(k)
• Paid company holidays
• Paid time off
• Paid parental and caregiver leave
• Plus more! See benefits here for additional details.