AI Infrastructure Is the Hiring Gap Companies Can't Afford to Miss

As organizations develop generative AI applications, autonomous agents and large language models, attention is understandably focused on AI engineering and model development. Yet behind every successful AI application sits an infrastructure layer responsible for moving data, supporting compute requirements and allowing models to perform reliably at scale. 

For many businesses, that creates an emerging talent challenge. 

Charlie Tulio, a Consultant at Glocomms, sees infrastructure as a key part of the AI talent equation, particularly as organizations move from developing models to deploying them at scale:

Infrastructure specialists provide the foundations that allow AI models to process growing volumes of data and operate reliably as demand increases. For technology leaders, this means infrastructure hiring should form part of the AI strategy from the outset, rather than becoming a priority only when models approach production. 

Why infrastructure matters to AI performance 

An AI model cannot deliver business value independently of the technology environment around it. 

Data has to be collected, processed, stored and moved through systems efficiently. Compute resources must support demanding workloads, and applications need the architecture to scale as usage grows. AI teams also need reliable environments in which engineers and data scientists can develop and deploy their work. 

The infrastructure challenge becomes more significant as organizations move beyond experimentation. 

Charlie believes this is an area some businesses have yet to address fully:

The concern is that organizations can become highly focused on what a model can do today without asking what happens when significantly more data, users and workloads pass through the same systems tomorrow. 

Charlie adds:

That makes infrastructure capacity a business issue as much as a technical one. An impressive AI proof of concept means little if the underlying environment cannot support production workloads reliably. 

The AI infrastructure talent companies need 

One difficulty for hiring teams is that AI infrastructure sits across areas that have traditionally been treated as separate talent markets. 

Organizations still need specialists across cloud, DevOps, platform engineering, infrastructure engineering, site reliability engineering and data engineering. Glocomms' cloud & infrastructure practice regularly supports organizations hiring across these disciplines. 

AI adds another layer to those requirements. 

Companies increasingly need people who understand how infrastructure decisions affect machine learning workloads, data pipelines and AI applications. Depending on the organization's architecture and objectives, that can mean experience spanning areas such as cloud platforms, distributed systems, data engineering, GPU infrastructure, platform engineering and machine learning systems. 

The strongest profile may therefore be difficult to capture with a conventional job title. 

Charlie has seen this intersection first-hand: 

This crossover matters because the people responsible for AI infrastructure must understand what engineering and data science teams are trying to achieve, while also building systems capable of supporting those ambitions. 

Businesses should consequently start with the problem they need the hire to solve, rather than if an "AI Engineer" title will attract the appropriate profile. 

The risk of delaying AI infrastructure hiring 

Infrastructure can be easy to deprioritize while AI initiatives remain relatively small. The problem comes when those initiatives begin to scale. 

Waiting can create technical and talent risks at the same time. 

From a technology perspective, infrastructure that was sufficient during development may struggle with larger data volumes, more complex workloads or increased customer usage. Retrofitting architecture later can also leave organizations solving scaling problems while simultaneously trying to progress their AI roadmap. 

From a hiring perspective, delaying creates another challenge. Organizations may eventually find themselves competing for professionals whose combination of infrastructure, software, data and AI expertise is already difficult to find. 

There is a wider retention consideration too. Charlie reports speaking with technology professionals who want to move because their current employers are not progressing quickly enough with AI:

For employers, that means AI capability can influence attraction and retention as well as product development. Experienced professionals may be drawn towards environments where they can work on current AI problems, take greater technical ownership, and contribute directly to new systems. 

Businesses that delay hiring therefore risk facing two pressures simultaneously: greater technical requirements as their AI estate expands, and stronger competition for the people capable of supporting it. 

Defining the right AI role before going to market 

One of the biggest challenges may begin before the search itself. 

AI job titles are evolving quickly, and organizations do not always have an established framework for deciding whether they need an AI engineer, machine learning engineer, data scientist, data engineer, or infrastructure specialist. 

Charlie has encountered this ambiguity directly: 

A broad AI title can create a search that targets the wrong talent pool, particularly when the actual business requirement sits at the intersection of several technical disciplines. 

Before hiring, organizations should define what the person will actually own. Is the priority developing models, creating AI agents, building data pipelines, scaling machine learning workloads, designing platforms, improving production reliability, or connecting several of these areas? 

That distinction can materially change the talent profile, experience requirements, and compensation needed. 

Charlie explains:

Greater precision at the beginning of the process can help companies enter the market with a clearer proposition and focus their search on professionals whose experience matches the underlying technical challenge. 

How Glocomms helps companies build AI teams 

AI hiring increasingly crosses traditional technology specialisms. A company may need software and machine learning expertise to develop an application, data specialists to support its models, and cloud and infrastructure professionals to make the entire environment scalable. 

Glocomms works across these technology talent markets, including Software & Analytics and Cloud & Infrastructure, giving clients access to specialist market knowledge across interconnected hiring requirements. 

The process can begin before a vacancy goes live. 

By working with hiring leaders to define responsibilities, assess the relevant talent market and establish realistic requirements, Glocomms can help organizations determine which profile aligns with the work that needs to be delivered. 

Market insight can also support decisions around compensation, available skill sets, competitor activity and the seniority required for a role. 

This becomes particularly valuable when organizations are hiring for emerging positions without an established internal benchmark. 

As Charlie puts it, defining the requirement early means specialist talent partners can “hit the ground running” rather than spending the opening stages of a search establishing what the business actually needs. 

For companies investing heavily in AI, infrastructure talent should be considered alongside models, applications, and data from the beginning. 

The organizations that build that capability early will be better positioned to turn AI projects into systems that can perform reliably as demand grows. 

Building an AI team or assessing the infrastructure skills your organization will need? Contact Glocomms to discuss your hiring requirements and the technology talent market.

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