AI Supercompute, NVIDIA Infrastructure Solutions Architect | Cloud & Engineering

You will work with clients to understand their AI workloads, performance objectives, growth plans, operational requirements, and facility constraints. You will translate these requirements into validated, implementation-ready architectures and detailed bills of materials across GPU compute, high-performance storage, networking, racks, power, cooling, and the platform stack required to operate clusters reliably at scale. You will be also acting as leading architect during deployment, collaborating tightly with customer and internal teams.

As a repeatedly awarded NVIDIA Consulting Partner of the Year in EMEA, we hold one of the deepest and most recognized NVIDIA partnerships in the region. This gives our architects privileged access to adoption programs, reference architectures, and NVIDIA engineering teams.

This is a role for an experienced architect who wants to move beyond executing predefined projects and take ownership of defining complete AI infrastructure solutions. You will shape technical decisions early, establish the design direction, align specialist vendors and delivery teams, and remain accountable for the solution through deployment and acceptance.

Requirements:

• At least 8+ years of hands-on experience in infrastructure, HPC, or data center architecting.
• Ability to turn incomplete or ambiguous requirements into explicit technical decisions, architecture principles, design artefacts, implementation plans, and measurable acceptance criteria.
• Strong practical, hands-on depth in at least one core infrastructure discipline, such as GPU and server infrastructure, enterprise or high-performance networking, storage, Linux and cluster platforms, cloud infrastructure, automation, or data center facilities.
• You are not expected to have expert-level knowledge across every component of AI supercomputing infrastructure. However, you should have experience in developing breadth holistic solutions across compute, storage, high-performance networking, physical data center infrastructure, and cluster platforms.
• Strong understanding of server architecture, including CPU, GPU, memory, PCIe, NVMe, NICs, BIOS and firmware configuration, remote management, hardware lifecycle management, and fault isolation.
• Good understanding of data center fundamentals, including rack design, power distribution, redundancy, power density, cooling, structured cabling, physical installation, and operational readiness.
• Working knowledge of high-performance networking concepts and technologies, for example: InfiniBand, RDMA, RoCE, GPUDirect RDMA, or equivalent HPC and low-latency interconnects.
• Understanding of storage concepts relevant to AI and high-performance workloads,
• Familiarity with Linux administration and troubleshooting, including system services, networking, storage, drivers, package management, logs, performance diagnostics, and automation.
• Practical familiarity with Kubernetes, Slurm, or equivalent cluster platforms. Enough to understand node onboarding, GPU scheduling, workload placement, networking, persistent storage, and day-2 operational requirements.
• Experience in working with technology vendors, delivery partners, customers, or cross-functional engineering teams.
• Strong written and verbal communication skills, including the ability to explain technical trade-offs to engineering teams, vendors, customers, and senior decision-makers.
• Comfort working in data center environments and reviewing physical infrastructure, server hardware, racks, cabling, power, cooling, and installation quality.
• Fluent English and Polish is a must

Bonus experience:

• Experience with NVIDIA AI infrastructure, including DGX, HGX, NVIDIA RTX PRO Servers, BasePOD, SuperPOD, DGX SuperPOD, NVIDIA AI Enterprise, or NVIDIA reference architectures.
• Familiarity with AI platform components such as Kubernetes, Slurm, NVIDIA GPU Operator, DCGM, NVIDIA Base Command Manager, Run:ai, Ray, Kubeflow, MLflow, vLLM, Triton Inference Server, or similar technologies.
• Experience designing or sizing high-performance storage environments, including parallel file systems, NVMe-over-Fabrics, Lustre, DDN, VAST Data, NetApp, Pure Storage or equivalent platforms.
• Familiarity with GPU workload characteristics, distributed training, collective communication patterns, NCCL, inference serving, LLM fine-tuning, RAG pipelines, and data-pipeline bottlenecks.
• Experience with liquid-cooled infrastructure, direct-to-chip cooling, high-density GPU racks, or data-centre design for AI and HPC workloads.
• Experience in consulting, technical presales, solution architecture, RFP delivery, or another customer-facing technical role.