{"id":22172,"date":"2026-09-02T00:00:53","date_gmt":"2026-08-26T09:08:38","guid":{"rendered":"https:\/\/jobtek.eu\/stanowisko\/senior-principal-ai-infrastructure-architect\/"},"modified":"2026-09-02T02:00:53","modified_gmt":"2026-09-02T00:00:53","slug":"ntt-data-inc-senior-principal-ai-infrastructure-architect-dublin-irlandia","status":"publish","type":"job_listing","link":"https:\/\/jobtek.eu\/en\/stanowisko\/ntt-data-inc-senior-principal-ai-infrastructure-architect-dublin-irlandia\/","title":{"rendered":"Senior Principal AI Infrastructure Architect"},"content":{"rendered":"

Make an impact with NTT DATA<\/strong><\/p>\n

Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion \u2013 it\u2019s a place where you can grow, belong and thrive.<\/p>\n

Your day at NTT DATA<\/strong><\/p>\n

The Senior Principal AI Infrastructure Architect is a highly skilled and advanced subject matter expert, responsible for leading the design of complex AI platform and managed-service solutions and driving the strategic vision and direction for the company’s largest enterprise clients. The role sits at the centre of NTT DATA’s AI Factories practice and is focused on the hardware foundations \u2014 GPU and accelerator compute, host CPU platforms, high-performance storage and AI fabric \u2014 that underpin enterprise-scale training, fine-tuning and inference workloads.<\/p>\n

Key Responsibilities:<\/strong><\/p>\n
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    \n
  • Lead the end-to-end design of large, complex AI infrastructure solutions \u2014 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 \u2014 for enterprise, sovereign AI and AI Factory clients.<\/li>\n
  • 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.<\/li>\n
  • Size and validate GPU clusters against real workloads \u2014 foundation-model pre-training, distributed fine-tuning, RAG, real-time and batch inference \u2014 using the right combination of 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).<\/li>\n
  • Define the supporting datacenter design: high-density power (50\u2013140 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.<\/li>\n
  • Work closely with the sales team to drive the presales process for AI infrastructure pursuits \u2014 client discovery, technical workshops, proposal writing, executive presentations and bid defence.<\/li>\n
  • Translate clients’ AI ambitions and business outcomes into a hardware and platform roadmap, positioning NTT DATA’s end-to-end portfolio \u2014 silicon, systems, storage, fabric, MLOps stack and managed services \u2014 to land service-led AI solutions.<\/li>\n
  • \u200bLead integration of\u00a0compute, storage, networking, the AI software stack (CUDA,\u00a0ROCm, Triton, NIM, NVIDIA AI Enterprise,\u00a0Run:ai,\u00a0Slurm, Kubernetes \/ Kubeflow) and managed-service operating models across multiple domains, delivery\u00a0units\u00a0and geographies.<\/li>\n
  • Build business cases,\u00a0TCO\u00a0and 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.<\/li>\n
  • Define architectural principles for AI infrastructure \u2014 accelerator\u00a0utilisation, data gravity, multi-tenancy, model lifecycle, energy efficiency \u2014 and apply them to influence architectural outcomes and governance.<\/li>\n
  • Develop As-Is, Vision, FMO and To-Be AI platform architectures,\u00a0identify\u00a0gaps and develop transition roadmaps.<\/li>\n
  • Synthesise\u00a0current and future trends in AI silicon, memory hierarchies (HBM3e, CXL),\u00a0interconnects\u00a0and AI software stacks with client strategic imperatives to create compelling, evidence-based solutions.<\/li>\n
  • Contribute to NTT DATA’s AI Factories knowledge base by sharing reference architectures, sizing tools and lessons learned with internal teams and clients.<\/li>\n<\/ul>\n

    Knowledge and Attributes<\/strong><\/p>\n

      \n
    • Deep, hands-on knowledge of\u00a0AI 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.<\/li>\n
    • Strong understanding of\u00a0AI-class storage: parallel filesystems, all-flash\u00a0NVMe\u00a0platforms, S3-class object stores, checkpoint and dataset pipelines and the I\/O patterns of large-scale training and inference (VAST, WEKA, DDN\u00a0EXAScaler, Pure\u00a0FlashBlade, NetApp ONTAP AI, Dell\u00a0PowerScale).<\/li>\n<\/ul>\n<\/div>\n
        \n
      • Solid command of\u00a0AI networking\u00a0\u2014 InfiniBand NDR\/XDR, RoCEv2, NVIDIA Spectrum-X, Ultra Ethernet,\u00a0NVLink\/NVSwitch\u00a0fabrics, congestion\u00a0control\u00a0and fabric design for rail-optimised\u00a0and fat-tree topologies.<\/li>\n<\/ul>\n<\/div>\n
          \n
        • Working knowledge of the\u00a0AI software and orchestration stack: CUDA,\u00a0cuDNN, NCCL,\u00a0ROCm, Triton Inference Server, NIM,\u00a0vLLM,\u00a0TensorRT-LLM,\u00a0Slurm, Kubernetes (with GPU Operator), Kubeflow,\u00a0Run:ai,\u00a0MLflow\u00a0and NVIDIA AI Enterprise.<\/li>\n
        • Familiarity with\u00a0datacenter facilities engineering\u00a0for AI workloads: high-density power, liquid cooling (DLC, rear-door, immersion), PUE\/WUE\u00a0optimisation\u00a0and the practical constraints of retrofitting existing\u00a0colo\u00a0space for accelerated compute.<\/li>\n
        • Excellent written and oral communication skills, with the ability to translate complex technical concepts for technical and non-technical executive audiences.<\/li>\n
        • Strong systems-thinking and strategic-thinking skills \u2014 able to capture the key elements of a system into a simple abstraction that empowers good decisions.<\/li>\n
        • Strong business financial skills, with the demonstrable ability to perform a cost-benefit analysis, build CAPEX vs OPEX\u00a0comparisons\u00a0and manage budgets.<\/li>\n<\/ul>\n
            \n
          • Knowledge of cloud, hybrid and sovereign AI deployment patterns, plus architectural governance for Agile,\u00a0DevSecOps\u00a0and\u00a0MLOps.<\/li>\n<\/ul>\n
              \n
            • Significant knowledge of core Managed Service portfolio artefacts, techniques, demos, tools and deliverables, applied to AI platform operations.<\/li>\n<\/ul>\n

              Academic Qualifications and Certifications:<\/strong><\/p>\n

                \n
              • Bachelor’s degree or equivalent in Information Technology, Engineering, Computer\u00a0Science\u00a0or\u00a0a related\u00a0field. Master’s or PhD\u00a0advantageous.<\/li>\n<\/ul>\n
                  \n
                • Vendor and technology certifications in AI infrastructure highly desirable \u2014 for example NVIDIA-Certified Associate \/ Professional (AI Infrastructure, AI Operations), Dell Technologies AI Factory, Cisco \/ Nutanix \/ HPE accelerated compute, Red Hat OpenShift AI,\u00a0Run:ai\u00a0\u2014 plus relevant storage and networking certifications.<\/li>\n<\/ul>\n
                    \n
                  • Scaled Agile certification advantageous.<\/li>\n<\/ul>\n

                    Required experience:<\/strong><\/p>\n

                      \n
                    • Significant experience\u00a0in a consulting,\u00a0presales\u00a0or architecture role within a large-scale (preferably multi-national) technology services environment, with\u00a0a track record\u00a0of leading AI infrastructure pursuits.<\/li>\n<\/ul>\n
                        \n
                      • Demonstrable experience designing and delivering production AI platforms \u2014 from single multi-GPU servers through to multi-rack training clusters and inference factories.<\/li>\n<\/ul>\n
                          \n
                        • 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.<\/li>\n<\/ul>\n
                            \n
                          • Proven ability to translate AI workload requirements (model size, parameter count, sequence length, throughput SLOs, latency targets) into\u00a0accurate\u00a0hardware bills of materials and sizing justifications.<\/li>\n<\/ul>\n
                              \n
                            • Significant client engagement and consulting experience, including client needs assessment, change management and the ability to\u00a0identify\u00a0whitespace for follow-on AI infrastructure and managed-services work.<\/li>\n<\/ul>\n
                                \n
                              • Significant business development and presales experience on infrastructure-led deals, ideally including sovereign AI, AI\u00a0Factory\u00a0or regulated-industry GenAI\u00a0programmes.<\/li>\n<\/ul>\n
                                \n
                                  \n
                                • Strong understanding of how AI infrastructure integrates with business processes, applications, data\u00a0platforms\u00a0and existing enterprise architecture.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

                                  Make an impact with NTT DATA Join a company that is pushing the boundaries of what is possible. 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