Systems Architect – AI Infra & GPU

DeFi Tech; Asia; Remote; PEN 168,000 - 281,000; Full time

Job summary

DeFiTech is seeking an AI Infrastructure & GPU Systems Architect with 10+ years of hands-on GPU infrastructure experience. You will own workload assessment, architecture, deployment, and optimization for enterprise AI platforms and data centers.

Your scope includes high-density GPU servers, liquid cooling, Linux administration, containerization, and orchestration across on-prem, colocated, cloud, and hybrid environments. Travel may be required for projects.

Qualifications

• Senior-level experience architecting and personally implementing production GPU infrastructure for AI or HPC.
• Deep expertise in at least one major GPU ecosystem, such as NVIDIA CUDA or AMD ROCm.
• Evidence of solving compute, memory, networking, storage, and thermal constraints.

Responsibilities

• Turn workload requirements into infrastructure architectures and capacity plans.
• Design and build GPU platforms from servers to distributed clusters across on-prem, colocation, cloud, and hybrid environments.
• Evaluate technologies against workload performance, cost, reliability, and compatibility.
• Lead implementation: hardware, firmware, drivers, OS, GPU software, and orchestration platforms.
• Validate performance with real workloads and tune for throughput, latency, and energy efficiency.
• Produce architecture diagrams, BOMs, deployment guides, and handovers; collaborate with AI teams and data center staff.

Skills

GPU infrastructure

HPC

Linux administration

Kubernetes/Slurm

Automation

Performance tuning

Vendor evaluation

Education

Engineering or computing degree

Tools

NVIDIA CUDA

AMD ROCm

SLURM

Kubernetes

Docker

Job description

Dubai / Abu Dhabi / Remote . Full Time | Flexible – AI Infrastructure & GPU Systems Architect with 10+ years and must have AI data center builds design experience.

Goal: Design the architecture. Build the systems. Prove the performance.

The Opportunity:

At DeFiTech, we are looking for an AI Infrastructure & GPU Systems Architect with the practical depth of an expert GPU Infrastructure Engineering. You will take ownership from workload assessment and architecture through deployment, optimization, and production readiness. This role requires strong foundations across AI compute, GPU memory, high-performance networking, storage, and liquid cooling, with the ability to make these components work together as a complete system.

Your scope will span enterprise AI platforms, Sovereign Infrastructure, GPU cloud environments, and AI data centers—supporting complex training, fine-tuning, inference, and high-performance computing workloads across different vendors and deployment scenarios.

What you will own:

• Turn workload requirements into infrastructure. Translate model size, data volume, concurrency, latency, and growth targets into practical architectures, capacity plans, and equipment specifications.
• Design and build GPU platforms. Deliver solutions ranging from individual GPU servers to distributed clusters across on-premises, colocation, cloud, and hybrid environments.
• Select technologies on merit. Evaluate GPU, server, network, storage, and cooling options against workload performance, compatibility, reliability, availability, and total cost of ownership.
• Lead implementation. Configure and integrate hardware, firmware, drivers, operating systems, GPU software, and orchestration platforms; resolve issues across the complete stack.
• Validate performance under real workloads. Establish acceptance benchmarks, identify bottlenecks, and tune systems for throughput, latency, utilization, stability, and energy efficiency.
• Deliver systems that teams can operate. Produce clear architecture diagrams, bills of materials, deployment guides, monitoring requirements, and operational handovers. Work closely with AI teams, vendors, and data center specialists.

The technical depth you bring

Liquid Cooling & Data Center Integration
Practical experience integrating high-density GPU systems with direct-to-chip liquid cooling. Understand rack power and heat loads, cooling distribution units, coolant loops, flow and temperature requirements, leak detection, and redundancy. Coordinate commissioning and facility interfaces with mechanical, electrical, and cooling specialists.

Platform Engineering & Operations
Strong Linux administration and troubleshooting skills, with practical experience in containers, Kubernetes and/or Slurm, automation, and infrastructure monitoring. Build for secure access, resource isolation, fault recovery, and maintainable operations.

What we are looking for

• Proven senior-level experience architecting and personally implementing production GPU infrastructure for AI or HPC.
• Deep expertise in at least one major GPU ecosystem, such as NVIDIA CUDA or AMD ROCm, with demonstrated ability to evaluate and integrate alternatives.
• Evidence of solving difficult problems across compute, memory, networking, storage, and thermal constraints.
• Ability to explain technical decisions clearly, document trade-offs, and lead delivery across engineering teams and technology partners.
• A relevant engineering or computing degree, or equivalent practical experience.

Additional strengths: Deployments across multiple GPU vendors, GPU cloud or multi-tenant platforms, large-scale cluster commissioning, and measurable improvements in workload performance or operating cost.

Job Summary

Location
Dubai (UAE) / Abu Dhabi (UAE) / Remote (Must Travel for Projects)

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