Senior Principal AI/HPC Architect

Stack

Responsibilities

• ▹Lead the end-to-end design of large, complex AI infrastructure solutions for enterprise, sovereign AI and AI Factory clients, covering accelerated compute (NVIDIA H100/H200/B200 and GB200 NVL72, AMD Instinct MI300X/MI325X, Intel Gaudi 3), CPU host platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), high-throughput storage tiers and lossless AI fabric
• ▹Architect reference designs built on NVIDIA DGX/HGX SuperPOD, Dell AI Factory with NVIDIA, Cisco Nexus HyperFabric AI, HPE / Lenovo / Supermicro accelerated compute and equivalent platforms, balancing single-node performance with cluster-scale efficiency
• ▹Size and validate GPU clusters against real workloads (foundation-model pre-training, distributed fine-tuning, RAG, real-time and batch inference) using NVLink/NVSwitch domains, InfiniBand NDR/XDR or Ultra Ethernet / NVIDIA Spectrum-X fabrics and tiered NVMe and parallel storage (VAST, WEKA, DDN, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)
• ▹Define the supporting datacenter design: high-density power (50-140 kW/rack), direct-to-chip and rear-door liquid cooling, structured cabling for AI fabrics and modular deployment models across on-prem, colo and sovereign-cloud footprints
• ▹Work closely with the sales team to drive the presales process for AI infrastructure pursuits: client discovery, technical workshops, proposal writing, executive presentations and bid defence
• ▹Translate clients' AI ambitions and business outcomes into a hardware and platform roadmap, positioning NTT DATA's end-to-end portfolio (silicon, systems, storage, fabric, MLOps stack and managed services)
• ▹Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA AI Enterprise, Run:ai, Slurm, Kubernetes / Kubeflow) and managed-service operating models across multiple domains, delivery units and geographies
• ▹Build business cases, TCO and unit-economics models (cost per token, cost per training run, GPU-hour economics) and end-to-end transition roadmaps for cloud-to-private AI migrations and sovereign AI deployments
• ▹Define architectural principles for AI infrastructure (accelerator utilisation, data gravity, multi-tenancy, model lifecycle, energy efficiency) and apply them to influence architectural outcomes and governance
• ▹Develop As-Is, Vision, FMO and To-Be AI platform architectures, identify gaps and develop transition roadmaps
• ▹Synthesise current and future trends in AI silicon, memory hierarchies (HBM3e, CXL), interconnects and AI software stacks with client strategic imperatives to create evidence-based solutions
• ▹Contribute to NTT DATA's AI Factories knowledge base by sharing reference architectures, sizing tools and lessons learned with internal teams and clients

Requirements

• ▹Deep, hands-on knowledge of AI hardware: GPU and accelerator portfolios (NVIDIA Hopper / Blackwell, AMD MI300/MI325, Intel Gaudi 3, emerging custom silicon), host CPU platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), system topologies (HGX, DGX, MGX, OAM) and how each choice maps to specific AI workloads
• ▹Strong understanding of AI-class storage: parallel filesystems, all-flash NVMe platforms, S3-class object stores, checkpoint and dataset pipelines and the I/O patterns of large-scale training and inference (VAST, WEKA, DDN EXAScaler, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)
• ▹Solid command of AI networking: InfiniBand NDR/XDR, RoCEv2, NVIDIA Spectrum-X, Ultra Ethernet, NVLink/NVSwitch fabrics, congestion control and fabric design for rail-optimised and fat-tree topologies
• ▹Working knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise
• ▹Familiarity with datacenter facilities engineering for AI workloads: high-density power, liquid cooling (DLC, rear-door, immersion), PUE/WUE optimisation and the practical constraints of retrofitting existing colo space for accelerated compute
• ▹Excellent written and oral communication skills, with the ability to translate complex technical concepts for technical and non-technical executive audiences
• ▹Strong systems-thinking and strategic-thinking skills, able to capture the key elements of a system in a simple abstraction that empowers good decisions
• ▹Strong business financial skills, with the demonstrable ability to perform a cost-benefit analysis, build CAPEX vs OPEX comparisons and manage budgets
• ▹Knowledge of cloud, hybrid and sovereign AI deployment patterns, plus architectural governance for Agile, DevSecOps and MLOps
• ▹Significant knowledge of core Managed Service portfolio artefacts, techniques, demos, tools and deliverables, applied to AI platform operations
• ▹Bachelor's degree or equivalent in Information Technology, Engineering, Computer Science or a related field
• ▹Significant experience in a consulting, presales or architecture role within a large-scale (preferably multi-national) technology services environment, with a track record of leading AI infrastructure pursuits
• ▹Demonstrable experience designing and delivering production AI platforms, from single multi-GPU servers through to multi-rack training clusters and inference factories
• ▹Strong working knowledge of the AI hardware vendor landscape (NVIDIA, AMD, Intel, Dell, HPE, Lenovo, Supermicro, Cisco, Pure, VAST, WEKA, DDN, NetApp) and how to position partner ecosystems competitively
• ▹Proven ability to translate AI workload requirements (model size, parameter count, sequence length, throughput SLOs, latency targets) into accurate hardware bills of materials and sizing justifications
• ▹Significant client engagement and consulting experience, including client needs assessment, change management and the ability to identify whitespace for follow-on AI infrastructure and managed-services work
• ▹Significant business development and presales experience on infrastructure-led deals, ideally including sovereign AI, AI Factory or regulated-industry GenAI programmes
• ▹Strong understanding of how AI infrastructure integrates with business processes, applications, data platforms and existing enterprise architecture

Nice to have

• ▹Master's or PhD
• ▹Vendor and technology certifications in AI infrastructure, for example NVIDIA-Certified Associate / Professional (AI Infrastructure, AI Operations), Dell Technologies AI Factory, Cisco / Nutanix / HPE accelerated compute, Red Hat OpenShift AI, Run:ai, plus relevant storage and networking certifications
• ▹Scaled Agile certification

Soft skills

What we offer

• ▹Remote working

About the company

NTT DATA is a business and technology services leader with over $30 billion in revenue, serving 75% of the Fortune Global 100. It is part of NTT Group, which invests over $3 billion a year in R&D, and has experts in more than 50 countries.