[Hiring] AI Supercompute Infrastructure Solutions Architect @Deloitte

Role Description

We are a technology consulting firm building and operating next-generation AI supercompute infrastructure for the world’s most ambitious organizations. As an AI Infrastructure Solutions Architect, you will define and guide the delivery of complete AI supercomputing solutions.

• Work with clients to understand their AI workloads, performance objectives, growth plans, operational requirements, and facility constraints.
• 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.
• Act as leading architect during deployment, collaborating tightly with customer and internal teams.
• Shape technical decisions early, establish the design direction, align specialist vendors and delivery teams, and remain accountable for the solution through deployment and acceptance.

Qualifications

• 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.
• 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.
• 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.

Requirements

• 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.

Responsibilities

• Design and build AI platform environments—from virtualization, operating systems, networking, and automation to Kubernetes, Slurm, and containers.
• Configure and optimize high-performance GPU networking (InfiniBand, RoCE, NVLink, NVSwitch, GPUDirect RDMA, NCCL).
• Monitor and improve platform health using tools like Prometheus, Grafana, and DCGM.
• Own the platform architecture, ensuring both logical and physical infrastructure are designed and deployed to the highest standards.
• Lead technical discussions with clients, translate business needs into platform designs, and support pre-sales activities.
• Drive projects end-to-end—from architecture and deployment to optimization and ongoing platform evolution.

Benefits

• Flexible working hours.
• Permanent employment or contract.
• Medical and health insurance.
• Multisport and other lifestyle benefits.
• Language courses.
• Friendly coworkers & team spirit.
• Multiple geographies and clients.
• Work for well-known brands.
• Exposure to trailblazing business and technology projects.
• A place in the first line of a digital transformation.
• Everyday opportunities to influence how and where we do our business.
• A development path to fit your needs.