Key skills
NVIDIA GPU platformsAI/ML frameworksCloud AI servicesHigh-speed networkingPre-sales campaignsMLOps platformsAnalytical skillsClient guidanceDatacenter securityEmerging technologiesTechnical presentationsAIMLLLMPyTorchTensorFlowMLOpsMLflowKubeflowAnalyticsAWSAzureGCPKubernetesSageMakerVertex AICI/CDSales
About this role
AHEAD builds platforms for digital business and is seeking a Senior Modern Datacenter Specialist Solutions Engineer (SSE) to focus on designing and enabling enterprise-scale AI infrastructure solutions. The role involves leading technical sales motions, partnering with various stakeholders, and developing market strategies for AI initiatives in the US enterprise market.
Responsibilities:
- 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
- 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
- US work authorization required; US citizenship preferred for certain regulated clients
- Willingness to travel for client engagements and partner events
- NVIDIA Certified Systems Engineer / AI Practitioner; NVAIE enablement badges
- Kubernetes CKA/CKS; GPU Operator experience
- AWS/Azure/GCP Architect; relevant AI/ML specialty