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AI infrastructure.
End to end.

AI and GPU roles are the most contested hires in the market. We search every layer of the stack worldwide, from the power feed to the people who sell the compute.

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Seven layers.
One search partner.

An AI campus is only as fast as its slowest hire. We recruit across the whole stack, so the power lead, the cooling engineer and the GPU cloud CFO come from one coordinated search.

GPU servers with direct-to-chip liquid cooling hoses and manifolds
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  1. Leadership

    The executives building AI infrastructure businesses.

    • CEO, COO and CTO, AI infrastructure platforms
    • CFO and capital markets
    • Board and non-executive directors

  2. Commercial

    The people who sell and price compute.

    • VP Sales, GPU cloud
    • Head of AI partnerships
    • Capacity and pricing leadership

  3. Platform & Product

    GPU-as-a-service, from roadmap to customer.

    • Head of GPU cloud product
    • AI platform engineering leadership
    • Capacity planning

  4. GPU Cluster

    Racking, networking and running the clusters.

    • Head of GPU infrastructure
    • Cluster deployment and operations
    • HPC and AI network architecture

  5. Build & Commission

    AI campuses delivered on compressed timelines.

    • AI campus programme directors
    • Heads of commissioning and IST
    • Construction and package leadership

  6. High-Density Design

    Liquid cooling and power density the old pipeline never trained for.

    • Liquid cooling and direct-to-chip leads
    • High-density MEP design
    • Thermal engineering

  7. Power & Site

    Megawatts first. Everything else waits on the grid.

    • Power and grid strategy
    • Energy procurement
    • Land and site acquisition

Who is hiring for AI infrastructure.

  • Hyperscalers & cloudRegional AI build-out teams, high-density design and the operations leadership to run it.
  • Neoclouds & GPU cloudsFounding and scaling teams for providers standing up GPU capacity at speed.
  • Telecommunications operators & sovereign AINew AI and GPU business units inside operators, often with national mandates.
  • Developers & investorsLeadership for AI-ready campuses and the platforms funds are backing.
  • Engineering & OEMsCooling, power and equipment specialists scaling to match AI demand.
Our AI and GPU cloud mandates

Why these are the hardest hires in the market.

  1. The pipeline barely exists

    Liquid cooling moved from niche to standard in about two years, with no dedicated training route behind it.

  2. Everyone is hiring the same people

    Hyperscalers, neoclouds, telecommunications operators and funds compete for the handful who have delivered AI capacity before.

  3. The best candidate is often adjacent

    Many AI infrastructure leaders come from traditional facilities, HVAC, HPC or cloud operations. Finding them takes mapping, not job boards.

76%

Measure
Share of AI server deployments that are liquid cooled in 2026, up from 15% in 2024.
Source
Goldman Sachs, 2025

$6.7T

Measure
Capital needed globally for AI and traditional data centre capacity through 2030.
Source
McKinsey, 2025
Two infrastructures, one shortage

Building capacity?
Build the team first.

Tell us what you're building and the role that can't wait.

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