Hiring AI Infrastructure and Connectivity Talent

AI is reshaping data center demand. The market is no longer scaling only for cloud capacity. It is now expanding for high-density compute, low-latency connectivity, secure network architecture and always-on performance.

For employers, this creates a new hiring challenge. AI-ready data centers need people who can build, connect, secure and operate complex technology environments at speed and scale. The demand is clear, but the talent pool is limited.

AI infrastructure investment is moving fast

Investment in AI data center platforms is accelerating. In June 2026, KKR launched Helix Digital Infrastructure, an AI infrastructure company with more than $10 billion in committed capital, alongside the Kuwait Investment Authority, NVIDIA and Vistra. The venture brings together data centers, power and AI technology partnerships to support hyperscale demand.

Power is also becoming a major constraint. S&P Global reports that US data center demand is expected to reach 75.8 GW in 2026, 108 GW in 2028 and 134.4 GW in 2030, covering IT equipment, cooling, lighting and other uses.

This changes the hiring brief. When power, performance and uptime are under pressure, employers need technology teams that can improve efficiency across cloud, network, infrastructure and security environments. That is increasing demand for cloud engineers, network specialists, site reliability engineers, AI infrastructure engineers and cybersecurity professionals.

Connectivity is now part of the AI infrastructure brief

AI workloads depend on fast, stable and secure movement of data. Latency, bandwidth, routing, segmentation and network resilience all affect performance.

That places network talent closer to the center of data center strategy. Employers need professionals who can support network architecture, cloud connectivity, high-performance networking, automation, network security, observability, resilience planning and interconnect strategy.

Weak connectivity can limit the value of AI infrastructure investment. Strong connectivity helps turn capacity into business performance.

The infrastructure and AI skills gap

The hardest roles to fill often sit between traditional infrastructure and AI engineering.

Hailey Thompson, Consultant at Glocomms, said companies are seeing a gap between professionals who understand infrastructure and professionals who understand AI:

“You find people who do infrastructure without AI, and you find AI engineers, but combining those skillsets is where companies are seeing a gap.”

That gap matters. AI-ready data centers need people who understand cloud, networks, data pipelines, GPU infrastructure, storage, security and operational resilience. Hiring for one skillset in isolation can leave pressure elsewhere in the environment.

Charlie Tulio, Consultant at Glocomms, also highlighted the importance of looking beyond model development:

“AI needs to scale, and there is a lot of information going into these models. The infrastructure, the data pipelines and the backbone behind model development all have to come together for a company to succeed.”

The roles employers are competing for

Hiring demand is growing across AI infrastructure, cloud, network and cybersecurity teams. Some of the most competitive roles include:

Glocomms supports organizations hiring AI engineers, machine learning engineers, data engineers, data architects, software engineers and software architects. Its Cloud and Infrastructure practice supports cloud architects, site reliability engineers, cloud engineers, DevOps engineers, network engineers, infrastructure engineers and systems engineers.

Together, these skillsets form the technology layer behind AI-ready data centers.

Compensation is setting a higher bar

AI infrastructure and platform roles are already commanding premium packages in the US market.

Charlie noted that compensation expectations can quickly become a barrier when companies target talent from frontier AI businesses:

“There is a disconnect between wanting somebody from Anthropic or OpenAI, but not being willing to pay as much as somebody who is making those base salaries and total compensation.”

For employers, the message is direct. If the role requires rare AI infrastructure experience, the compensation strategy needs to reflect the market.

Location strategy matters

AI infrastructure talent is concentrated in specific markets.

For AI infrastructure engineers, ML platform engineers, AI platform engineers and AI systems architects, the strongest locations include San Francisco, Seattle and New York City. Austin and Boston are also active markets for mid-level AI infrastructure, MLOps and platform talent.

For AI security engineers, key markets include Washington DC, New York City and San Francisco.

For data center engineers with an AI or HPC focus, talent is more concentrated in Ashburn, Dallas, Phoenix and Chicago.

Employers should decide early whether a role needs to be on-site, hybrid or remote. Location flexibility can widen the market. For roles tied to physical infrastructure, employers need a sharper local talent strategy.

Role clarity speeds up hiring

AI hiring is still new for many organizations, and job briefs can become too broad. That slows the search and weakens candidate engagement.

Charlie said this is one of the most common issues he sees:

“With AI being so new, there is a lot of ambiguity around what clients want. Some say they need an AI engineer, but after defining the requirement, they actually need a data scientist. Being specific on the role from the start helps us move faster and reach the right talent.”

This is particularly important for data center employers. An AI infrastructure engineer, ML platform engineer, GPU engineer, HPC engineer and AI systems architect may all support AI growth, but they solve different problems.

The strongest hiring processes define the technical outcome first, then shape the role around it.

Cybersecurity cannot be added later

AI infrastructure creates new security questions. Who can access workloads, models and data? How are cloud environments monitored? How are network controls applied without slowing performance? How are incidents contained in high-density environments?

Cybersecurity needs to be built into AI infrastructure hiring from the start.

Glocomms’ cybersecurity practice supports roles across cloud security, threat intelligence, security operations, incident response, DevSecOps, application security and cyber risk. For data center organizations, these capabilities are central to resilience.

The people behind AI-ready data centers

AI infrastructure depends on more than capital, power and compute. It depends on people who can connect, secure and operate high-density environments.

Glocomms supports organizations hiring the technology talent behind AI-ready data centers, across AI infrastructure, cloud, network, software, data and cybersecurity.

As part of Phaidon International, Glocomms can also connect data center organizations with specialist talent brands across finance, legal, supply chain, engineering and infrastructure.

Speak to Glocomms about your AI infrastructure and connectivity hiring needs, or explore how Phaidon International’s specialist brands can support your wider talent strategy.