AI Infrastructure Lead – GPU & AI Compute

About the Role:

We are looking for an experienced AI Factory Infrastructure Lead to design, build, and operate the infrastructure foundation for our AI Factory / Token Factory.

This is a senior technical role for someone who understands how to architect GPU-based AI compute platforms at scale — from individual accelerator performance through to multi-rack clusters, high-performance networking, storage, power, cooling, orchestration, and operational reliability.

The successful candidate will help turn AI infrastructure ambition into a resilient, efficient, and scalable compute platform capable of supporting demanding training and inference workloads.

What You Will Do:

Lead GPU Technology and Architecture

• Maintain deep technical understanding of current and emerging GPU architectures.
• Evaluate different generations and configurations of NVIDIA and other relevant AI accelerators.
• Assess GPU and AI accelerator platforms across memory bandwidth, tensor performance, interconnect capability, training and inference performance, power efficiency, and total cost of ownership.
• Develop GPU technology and lifecycle roadmaps.

Design Scalable AI Cluster Architecture

• Design scalable GPU clusters ranging from smaller development environments to large-scale multi-rack systems.
• Design compute, networking, storage, control, and management layers.
• Architect the compute, networking, storage, control, and management layers required for high-performance AI workloads.

Shape Data-Center Engineering Requirements

• Define rack layouts and infrastructure requirements.
• Define power requirements, cooling approach, redundancy model, UPS requirements, and network topology for high-density AI infrastructure.
• Work closely with data-center and facilities teams.

Drive Capacity, Performance, and Utilization

• Develop capacity models for training and inference.
• Translate business demand into technical capacity requirements, including GPU hours, token throughput, memory, network, and storage requirements.
• Optimize GPU utilization and workload scheduling.
• Benchmark models and inference engines across alternative hardware configurations.

Operate Reliable AI Infrastructure

• Establish monitoring and operational processes for GPU utilization, memory, temperature, power, networking, workload performance, and hardware health.
• Establish lifecycle, maintenance, firmware, and driver-management processes.
• Define availability and disaster-recovery architecture.

Optimize Cost and Commercial Efficiency

• Build cost models covering cost per GPU hour, cost per million tokens, power cost, infrastructure utilization, and total cost of ownership.
• Compare on-premise, hosted, and public-cloud economics.

What We Are Looking For:

Required Experience and Capabilities

• Strong background in large-scale compute, HPC, GPU computing, cloud infrastructure, or data-center architecture.
• Deep understanding of GPU architecture and AI workloads.
• Strong understanding of high-performance networking and distributed systems.
• Experience designing compute clusters and production infrastructure.
• Strong knowledge of Linux, containers, Kubernetes, and infrastructure automation.
• Ability to perform detailed capacity and performance engineering.

Preferred Experience

• Hands-on experience with NVIDIA H100/H200/B-series or equivalent platforms.
• Experience with DGX, HGX, SuperPOD, or comparable AI infrastructure.
• Knowledge of CUDA, NCCL, TensorRT, Triton, vLLM or related technologies.
• Experience with InfiniBand, RDMA, NVLink, NVSwitch and high-speed Ethernet.
• Experience with direct-to-chip liquid cooling and high-density AI data centers.
• Experience designing clusters at several hundred GPUs or greater.

Why Join Us:

This is an opportunity to play a defining role in building a high-performance AI infrastructure platform from the ground up.

You will work at the intersection of AI, cloud, data-center engineering, and advanced GPU computing, with direct influence over the architecture, performance, reliability, and economics of a strategic AI platform.

Interested in building AI infrastructure at scale? Apply now and join us in shaping the next generation of AI infrastructure.