Khazna Introduces Thuraya Program to Strengthen UAE’s AI Infrastructure Talent Pipeline

Khazna Data Centres has launched a year-long graduate development programme aimed at building a domestic pipeline of data centre talent, as demand for artificial intelligence infrastructure accelerates across the Gulf and globally.

The Abu Dhabi-based hyperscale operator announced the launch of “Thuraya,” a structured training initiative developed in partnership with the International Data Center Authority (IDCA). The programme is designed to prepare early-career engineers for operational roles across Khazna’s engineering teams, special projects division and Khazna NexOps, its in-house mission-critical operations arm.

Thuraya will function as what the company describes as a data centre “finishing school,” combining technical instruction with supervised, hands-on exposure inside live facilities. The year-long structure links certification directly to operational readiness, meaning participants are assessed not just on theory but on their ability to execute safely in real environments.

Hassan Alnaqbi, CEO of Khazna Data Centers, stated the initiative as an investment in the human layer of the AI economy. “Thuraya is a practical investment in the people who will run the infrastructure behind the AI economy,” he said, adding that the programme is designed to embed safety discipline and operational standards as the company scales.

Why Data Centres Matter More Than Most People Realise

To understand why this matters, it helps to step back. When people think about AI, they often picture chatbots, self-driving systems or advanced software models. What is less visible is the physical infrastructure that makes those systems possible. AI runs on enormous clusters of servers housed inside specialised data centres  - buildings designed to manage extreme computational density, energy loads and cooling requirements.

Training and running AI models require far more power than traditional enterprise computing. The servers operate at high temperatures. Cooling systems are complex. Electrical redundancy must be flawless. A failure inside a data centre can disrupt financial systems, healthcare platforms, logistics networks or government services.

In other words, AI does not float in the cloud. It sits inside concrete facilities that must operate continuously and precisely.

As AI workloads grow, so does the technical complexity of these environments.

The Skills Gap Behind the AI Boom

Khazna, which positions itself as one of the fastest-growing hyperscale platforms globally, is expanding capacity to support the region’s AI ambitions. But scaling buildings is only one part of the equation.

Operating a hyperscale data centre is not the same as running a standard IT facility. Engineers must understand electrical systems, cooling architecture, fire safety, network redundancy, incident response protocols and compliance standards — often simultaneously. Mistakes can carry high financial and reputational costs.

Mehdi Paryavi, Chairman and CEO of IDCA, said structured training is essential for maintaining reliability at scale. “Programs like Thuraya matter because in-house teams are only as strong as the training behind them,” he said, noting that consistent standards help reduce operational risk.

Gabriella Planojevic, Khazna’s Learning & Talent Management Director, emphasised that the programme is built around measurable competency development tied directly to operational workflows.

This means the programme is not simply about education, it is about ensuring that the people running AI infrastructure can respond correctly when systems fail, power fluctuates or incidents occur.

What This Signals About the AI Economy

The launch of Thuraya reflects a broader shift in the global AI race. Until recently, much of the focus has been on building models and securing semiconductor supply. Governments and companies have competed to attract AI labs and chip manufacturers. But as AI systems move from experimentation to daily economic use, the reliability of underlying infrastructure becomes just as important as model performance.

Data centres are now strategic assets. Countries that want to position themselves as AI hubs must ensure they can build, operate and maintain these facilities safely and efficiently.

This is particularly relevant in the Middle East, where governments are investing heavily in digital transformation and AI-led growth. Infrastructure expansion has been rapid. The next challenge is institutional maturity - creating repeatable standards, local expertise and operational resilience.

In that sense, Thuraya represents a move from build-out to institutionalisation. It acknowledges that AI infrastructure is not sustainable without a structured talent pipeline.

From Talent Development to Strategic Capability

There is also a longer-term implication - AI workloads are projected to increase in density and energy demand. As this happens, operational failure becomes more consequential. A mismanaged outage in an AI-heavy economy could affect sectors far beyond technology.

By embedding certification pathways and linking them directly to operational responsibility, Khazna is attempting to reduce that risk internally. It is also positioning itself to compete globally, where uptime guarantees and operational discipline increasingly influence enterprise contracts.

For readers outside the infrastructure sector, the key takeaway is this: AI capability is not just about software innovation. It depends on power systems, cooling technology, safety protocols and trained engineers who can manage complex environments under pressure.

Thuraya reflects a deeper truth about the AI era. As digital systems become foundational to economic life, the people who operate the physical backbone of those systems become strategically important. The AI race is not only about smarter algorithms. It is also about who can keep the lights on.