Deloitte

AI Supercompute, NVIDIA Infrastructure Solutions Architect | Cloud & Engineering

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SeniorZdalnie·WarszawaFull-time

#467436·Dodano 8 dni temu·2

Źródło: nofluffjobs.com

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Tech Stack / Keywords

AIGPUStorageNetworkingLinuxCloudKubernetes Node.jsCommunication skillsKubeflow MLflowNetAppLLMRAG

Podsumowanie oferty

Define and guide the delivery of AI supercomputing infrastructure solutions for clients, translating workload and facility requirements into implementation-ready architectures. Lead solutions across GPU compute, high-performance storage and networking, and the platform stack, including Kubernetes and Slurm.

Wymagania

At least 8 years of hands-on experience in infrastructure, HPC, or data center architecting.
Ability to turn incomplete or ambiguous requirements into 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 developing 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 such as InfiniBand, RDMA, RoCE, and 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, including node onboarding, GPU scheduling, workload placement, networking, persistent storage, and day-2 operational requirements.
Experience 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.

Nice to have:

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, and 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, or Pure Storage.
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-center design for AI and HPC workloads.
Experience in consulting, technical pre-sales, solution architecture, RFP delivery, or another customer-facing technical role.

Obowiązki

Work with clients to understand their AI workloads, performance objectives, growth plans, operational requirements, and facility constraints.
Translate client 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.
Lead architecture during deployment, collaborating with customer and internal teams.
Define technical decisions and design direction early, align specialist vendors and delivery teams, and remain accountable through deployment and acceptance.
Design and build AI platform environments spanning virtualization, operating systems, networking, automation, Kubernetes, Slurm, and containers.
Configure and optimize high-performance GPU networking, including InfiniBand, RoCE, NVLink, NVSwitch, GPUDirect RDMA, and NCCL.
Monitor and improve platform health using tools such as Prometheus, Grafana, and DCGM.
Lead technical discussions with clients, translate business needs into platform designs, and support pre-sales activities.
Drive projects from architecture and deployment through optimization and ongoing platform evolution.

Benefity

Flexible working hours.
Medical and health insurance.
Multisport and other lifestyle benefits.
Language courses.

Elastyczne godziny

Opieka zdrowotna

Ubezpieczenie

Karta sportowa

Kursy językowe

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