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AHEAD, Inc. logo
AHEAD, Inc.

AHEAD, Inc. is an IT services and consulting company that is on a mission to “accelerate the impact of technology on business.” As an employer, the company is known for its cha

Specialty Solutions Engineer, AI - Modern Data Center

Location

United States

Posted

115 days ago

Salary

$250K - $300K / year

Seniority

Mid Level

Job Description

Specialty Solutions Engineer, AI - Modern Data Center

AHEAD, Inc.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. 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.

Job 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.
  • 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.

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