Which AI Roles Should Your GCC Hire in 2026? A 24-Month Hiring Sequence

Your GCC needs AI talent; that’s clear. But which AI roles do you hire first, when, and which ones can come from your existing teams?

You already know it can’t hurt to be precise.

McKinsey found most leaders are twice as likely to chart out what roles and skills their GenAI plan needs in writing.

So, which ones does your GCC need over the next 24 months?

This guide breaks down the AI roles, the hiring order, team sizes, and what you can bring in from other in-house teams.

But first, a quick snapshot:

Which AI roles are GCCs hiring for in 2026?

At BEEU TECH, we scanned 2208 live job postings across 314 GCCs in India and the UAE.

The methodology: We went beyond the titles. A title tells you what a job needs. The description tells you what the person actually does all day. 

No. of job descriptions read: 668 job descriptions in 3 GCCs.

Our findings:

1. Most AI hiring in GCCs is about predicting things

Look at the numbers:

917 job titles included the words “AI,” “machine learning,” and “data.”

So, what are they actually expecting these hires to do? 

Same three tasks every day: training models, building pipelines, and cleaning up training data.                                               

PyTorch and TensorFlow were everywhere in the JDs.

🔸 What that means for the team you are hiring:

This team’s primary purpose may not be to use Gen AI at scale to create text and images. At the core of their tasks might be what enterprises do best: turning your own data into something useful.

Meaning, this team builds stuff that scores, sorts, and forecasts. And answer questions like: Is this transaction fraud? How much stock will we need for March? Is this part of the line likely to be defective?

2. Half the work is just getting the data to work

Out of 54 roles we checked properly, 27 were just building data pipelines. Half. Another 14 were only preparing training data.

🔸 What that means for the team you are hiring:

It’s exactly why a data engineer belongs in your first three hires. No fancy AI model works until your have data, an AI system can work with.

3. Job titles tell only half of the story

Job titles won’t show you what the roles actually expect AI hires to do. Here are two examples from the same data.

Only 4.4% of titles said “agent. But when we read 54 JDs properly, 17 described checking output quality. And 13 were describing building agents. That’s almost one in four, under different job titles.

🔸 What that means for the team you are hiring:

Niche tasks in AI, like AI evaluation/AI Governance/AI Implementation haven’t completely emerged as unique job titles.

But are parts of other tasks in JDs. Meaning, hiring specialists may require a different approach.

This even applies to Agentic AI developers. People are getting hired in GCCs to build agentic systems but are expected to perform other tasks as well.

4. Tools and frameworks are getting standardized

We came across some 21 different tools. But two do most of the work.

PyTorch shows up in 24 of the jobs. TensorFlow in 22. Vector databases (16 JDs) and LangChain come next. AI evaluation was a task in 17 roles (but only 7 titles out of 917 mentioned it). Azure gets named 11 times. Amazon Bedrock, 4 times. Google Vertex AI, two.

🔸 What that means for the team you are hiring:

It’s great news! The foundations are getting set. Meaning, over time, you’ll have more people to hire who’ve already worked with these tools.

Plus, training your own people is safer because these frameworks will not go obsolete overnight.

What that means for the AI roles your GCC opens?

The AI roles your GCC needs start with what you plan your GCC to do in the first place.

The 4 AI roles your GCC’s first engineering team can start with

You’ve seen the data. Now, here’s how you can use that information.

These four roles provide a practical starting point for most GCCs developing AI products or internal AI systems.

You do not need to hire all four people in the first month. 

Product ownership can sit with existing product management teams.

QA and AI engineering can share AI evaluation. Plus, you may already have data engineers.

The 7 AI Roles Mature GCCs Are Hiring For

So, where's this all headed?

Which AI Roles To Hire, By GCC Stage

Most GCCs are of three types:

Either you’re a GCC setting up new teams,

Or one that’s scaling engineering teams.

Or have AI working. As a result, a flat list of eight roles is insufficient.

Find your stage below.

Here is the thing: if you haven't noticed already, all these AI roles fall into five groups.

Let's dive in.

The 5 Groups Every AI Role In A GCC Falls Into

Which AI role you hire depends on what your GCC is building, how many AI systems it runs, how good its data is, and how far along it is.

But if you want AI to actually work, you need to include all five groups.

1. AI Product Engineering

This area is where AI becomes a real working product, feature, or business workflow.

Group 1 AI Product Engineering
Role	What they do
AI Engineer	Builds the AI system itself. Works across software, models, APIs, and agents.
LLM Engineer	Works on language-model apps: retrieval, context, agents, prompting, and how models behave.
AI Application Developer	Puts AI inside apps your customers and staff already use.
Forward Deployed Engineer	Takes a working system to HQ, another office, or a customer, and makes it fit there.

The first three roles build the system. The fourth role takes it somewhere else and makes it work there.

Two words to take note of: 

Retrieval means the system checks company documents before it answers, so the answer comes from internal files. 

Agents mean AI that does things inside systems; it clicks buttons, writes files, and updates records.

What teams actually use might surprise you. 

We read 668 job descriptions in full. Out of the 54 confirmed AI job postings, PyTorch showed up in 24 roles, TensorFlow in 22. Vector databases came third at 16, ahead of LangChain at 15 and ahead of every managed cloud AI service. Classic machine learning still covers most of the work. And retrieval has moved from experiment to staffing plan.

↳ Hire when your GCC owns the AI products that customers touch. Build this group before a big MLOps or platform team.

2. AI Platforms

Once you run several AI systems, teams need shared infrastructure. Otherwise, every team builds the same thing over again.

Group 2 AI Platforms
Role	What they do
AI Platform Engineer	Builds the shared parts: model gateways, guardrails, access rules, model lists, tooling.
MLOps Engineer	Ships models, watches them, tracks versions, and keeps them reliable in production.
Infrastructure / Cloud Engineer	Provides the compute, network, storage, and uptime underneath.

Three more words in simple terms: A model gateway works like a door (an internal one) through which all AI requests from each team pass. 

Teams handle access, cost, and logging in that one place. 

Guardrails are automatic checks that prevent the AI from doing or saying anything it shouldn’t. 

Inference is what it costs, in money and time, to run the model each time someone asks it something.

The division between the two engineering roles matters. 

Platform engineers build shared parts that other teams reuse. 

MLOps engineers take the models, put them into production, and run them.

3. Data Foundation

AI is only as good as the data behind it.

Group 3 Data Foundation
Role	What they do
Data Engineer	Builds the data pipelines and storage AI and analytics run on.
Analytics Engineer	Turns raw business data into clean, consistent datasets teams can trust.
Data Quality Engineer	Watches whether the data is accurate, complete, and traceable back to where it came from.

Even the best AI engineers cannot work with data that is missing, messy, or locked away. Not for long, anyways.

↳ Hire when: bad data access or quality is slowing your AI work down.

4. Governance & Trust

Build governance while building the product. It costs a lot more to add it after launch.

NIST's AI Risk Management Framework treats governance as the layer that runs through everything else and makes the other three parts work: spotting risk, measuring it, and managing it (NIST AI RMF 1.0).

Group 4 Governance & Trust
Role	What they do
AI Security Specialist	Protects models, data, agents, logins, APIs, and connected systems.
Responsible AI / Risk Specialist	Handles risk checks, human oversight, testing, and records.
AI Governance Lead	Sets policy, approvals, controls, and who is accountable.

This role is particularly important when the work is regulated or has high stakes involved. The EU's high-risk list comprises eight areas. These include hiring, credit scoring, vital public services, and critical infrastructure. (EU AI Act, Annex III).

↳ Hire when: a regulated or business-critical AI system is heading for launch.

5. Domain AI Expertise

You can reuse the tech across industries. You cannot reuse the business knowledge.

Group 5 Domain AI Expertise
GCC type	Knowledge needed
Manufacturing	Industrial AI, computer vision, predicting breakdowns before they happen
Healthcare	Clinical workflows, medical data
Banking	Fraud, credit, risk, financial crime
Supply chain	Demand forecasting, logistics, stock levels

This role does not always need a new job title. Your existing domain experts can work alongside AI engineers if they already know the process.

↳ Hire when: the same specialist knowledge keeps coming up across several AI projects.

The 11 AI Roles Many GCCs Often Overlook

Some of the most useful AI roles sit in the gaps between engineering, product, data, security, and operations.

Hire only for the familiar titles, and you miss them. AI engineer, data scientist, ML engineer. Those three are where most plans stop.

The 24-Month Problem: Your Month-One Hire May Not Be There By Month Twenty

Most 24-month AI hiring plans fail for one simple reason.

In India, GCCs are seeing attrition in AI and cloud roles running at 18 - 25 %. Average tenure has dropped to just 18 - 24 months. For GCCs overall, it is 16%.

That changes everything. This means that the AI Engineer hired in month one will most likely leave before month 24. The team you end up with is rarely the one you started out with. That's the team that stayed behind.

Salary increments are not solving the problem. The average growth of the GCCs is about 9.8 percent. Top performers make 17.2% . Roles of AI and ML can go up to 21.1%. Even so, these roles have the highest exit rates.

The solution:

Which AI Roles Will Still Matter After 24 Months?

The AI job market is moving very fast. So it is important to know which roles will last and which ones will slowly merge into other jobs.

PwC's 2026 Global AI Jobs Barometer looked at more than a billion job postings across 27 countries.

Jobs that need AI skills are growing almost eight times faster than the overall job market. This means that 69% of job ads require AI skills, compared to only 9% for the overall job market.

Jobs requiring AI skills are also paying a 62% average pay premium (PwC 2026 Global AI Jobs Barometer).

PwC also found something particularly noteworthy. In jobs that are highly exposed to AI, companies are now looking for more judgment, leadership, and adaptability.

For hiring, the learning is clear. Focus on AI roles that will remain relevant even when tools and models change. And they will definitely change.

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The 24-Month Plan: Which AI Roles To Hire In Each Phase

Below is a simple 24-month plan for a new GCC starting from Stage 1. If the GCC is already in Stage 2 or 3, the early phases can come earlier.

This is an illustrative scenario. Team sizes are given as planning ranges.

Which AI Roles Can Be Filled From Inside

Do you actually need all new hires?

Not always. You can build some of these roles with your existing teams. It reduces hiring pressure and gives your current engineers a chance to move into better work. Market hires usually stay for 18 to 24 months. People you train internally tend to stay longer.

There is one more reason this matters. Very few companies are building AI talent from the bottom.

Three graduate roles in 917. Every center is trying to hire from the skilled bracket which is why the shortage never ends.

Creating an internal path is one of the few ways to actually build more talent instead of just competing for it.

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Hire, Augment, or AI? The GCC Leader's Playbook for Building Engineering Capacity

Training your own people is one of three ways to fill a role. This one weighs it against hiring outside and handing the work to AI, and says when each is the cheaper answer.

The EU AI Act: The Deadline That Can Reorder Your Hiring

Most of your AI hiring is driven by workload. Regulation is different.

It can force you to build governance, risk, records, security, and oversight earlier than you planned. If your GCC supports European businesses, the EU AI Act matters to you. And you need to read the dates carefully, because different rules start on different days. The dates also changed in July 2026. 

Do not treat 2 August 2026 as the deadline for all high-risk AI rules. This deadline applied until the EU published its Digital Omnibus on AI on 24 July 2026. It came into force on 27 July, just six days before the old deadline (Gibson Dunn).

Here is what applies when:

Two terms to remember:

◽ GPAI means general-purpose AI. These are large models that can do many different jobs.

◽ Annex III is the EU's list of eight high-risk uses. Like: hiring, credit scoring, essential public services, and running critical infrastructure

What this means for GCCs hiring for AI roles?

Do note that the deadline is 2027. So, there isn't much time to set up approvals, records, and oversight management.

7 Questions To Ask Before You Open An AI Role

Frequently Asked Questions

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Engineering Organization Design For GCCs: How To Go From 0 To 100 Engineers

You now have the roles and the order to hire them in. This is the shape they sit in once they arrive: reporting lines, team sizes, and templates you can copy.