There is a pervasive myth in the Global Capability Center (GCC) ecosystem: "AI talent = Big Tech budgets."

It’s an easy trap to fall into. When you see Meta or Google paying mid-level AI engineers $300k+ (often with significant stock components), it feels like the game is rigged against smaller players. For a small or emerging GCC, perhaps a team of 50 to 200 engineers building critical internal tools or customer-facing products, this mindset is paralyzing. It leads to the assumption that you can’t build a high-performance AI team without a blank check.

This assumption holds small GCCs back from even entering the arena. But the reality is that AI talent is not a monolith. Small GCCs don't need to outspend Big Tech; they need to outmaneuver them. By being focused on application rather than research, and by offering a level of autonomy that large organizations can't match, small GCCs can build world-class AI capabilities

Why Competing Head-On with Big Tech Doesn’t Work

Let’s look at the numbers. Recent data from 2025 indicates that GCCs in India are already paying a 30-40% premium for AI and GenAI roles compared to traditional IT services. However, Big Tech firms operate in a different stratosphere, often offering compensation packages (especially with RSUs) that are 2-3x the market average.

Entering a salary war with these giants is a losing game for a small GCC.

• The Brand Gap: You cannot compete on "prestige" in the traditional sense. A resume stamp from a FAANG company still holds outsized weight in the market.
• The Retention Trap: Even if you stretch your budget to hire a "Big Tech" profile, you often face high attrition. These candidates are often accustomed to vast infrastructure support and research-heavy mandates. Placing them in a lean, agile environment where they need to clean their own data and deploy their own models often leads to frustration and rapid exits.
• Slow Hiring: Trying to match Big Tech's hiring criteria (e.g., LeetCode hards, PhD requirements) usually results in 6-month hiring cycles that small teams cannot afford.

The smarter alternative is to stop playing their game entirely. Instead of competing on compensation and brand, compete on context and growth.

Redefining AI Talent for Small GCCs

A small GCC doesn't need a Research Scientist who publishes papers at NeurIPS. You need engineers who can ship products. It is critical to redefine the "AI Engineer" profile away from the academic/research archetype toward the "AI Product Engineer."

The 4 Categories of Practical AI Talent:

1. AI Product Engineers: Full-stack developers who know how to chain prompts, manage context windows, and integrate AI APIs into user interfaces. This is often the highest-ROI hire for a small GCC.
2. Applied ML Engineers: Engineers who can take an off-the-shelf model (e.g., Llama 3, BERT), fine-tune it on proprietary data, and deploy it.
3. Data Engineers with AI Exposure: The backbone of any AI operation. They build the pipelines that feed the models.‍
4. Prompt Engineers / LLM Developers: Specialists in squeezing performance out of foundation models without retraining them

Hire for Outcomes, Not Pedigree

Degrees from top-tier institutes (IIT/BITS) and "ex-Google" badges are poor predictors of success in an applied AI environment. In fact, they can sometimes be inverse signals for "scrappiness."

Small GCCs must pivot to outcome-based evaluation.

• The "Real-World" Take-Home: Instead of asking candidates to invert a binary tree on a whiteboard, give them a realistic 4-hour task.
  
  • Example: "Here is a messy CSV of customer support logs. Build a simple RAG (Retrieval-Augmented Generation) pipeline using Python and a vector database to answer questions based on this data. Dockerize the solution."
• Systems Thinking over Algorithms: Ask how they would design an AI feature for failure.
  
  • Question: "What happens when the LLM hallucinates? How do you measure 'bad' output in production? How do you handle latency spikes?"‍
• Imperfect Data Handling: Real-world data is never clean. Test their ability to handle null values, unstructured text, and ambiguity. A candidate who spends 3 hours cleaning data is often more valuable than one who spends 3 hours tweaking hyperparameters on a pre-cleaned dataset

Leveraging Talent Pools Big Tech Overlooks

Big Tech recruiters often fish in a very small pond: top-tier universities and competitors. This leaves massive reservoirs of talent untapped.

• The "Mid-Sized Product" Transitioners: Look for engineers coming from successful mid-sized product companies (startups or other GCCs) who have hit a "growth ceiling." They have rigorous engineering practices but are hungry for the ownership a small, new team provides.
• Tier-2 and Tier-3 City Talent: The rise of remote work and digital upskilling has democratized talent. Engineers from cities like Pune, Indore, or Coimbatore often possess strong fundamental skills and higher retention rates than their metro counterparts.‍
• Internal Transfers: Do not underestimate your existing Java or Python developers. An experienced backend engineer who learns AI concepts for 3 months often outperforms a fresh AI graduate because they understand production systems, CI/CD, and security

Building an AI-Ready Team Without Hiring Unicorns

You do not need to hire a "Head of AI" as your first hire. In fact, that's often a mistake.

The "Pod" Approach:

Instead of chasing one superstar, build a balanced "AI Pod":

• 1 Senior Lead (The Architect): Someone with 8+ years of experience in general software engineering who has pivoted to AI in the last 2-3 years. They ensure the system is scalable.
• 2 Mid-Level AI Product Engineers: Strong coding skills, comfortable with Python/JS, and eager to experiment with APIs and tools.
• 1 Data Engineer: Focused purely on data quality and pipelines.

Contractors & Fractional Experts:

For niche problems (e.g., "We need to optimize this specific CUDA kernel"), use high-end consultants or fractional experts. Don't hire a full-time specialist for a problem you only face once a quarter.

Positioning Small GCCs as a Career Accelerator

To attract top talent, you must sell what you have that Big Tech doesn't.

• "You Build It, You Own It": In a Big Tech firm, an engineer might own a small microservice. In a small GCC, they own the entire recommendation engine. This narrative appeals to builders.
• Visibility: Promise (and deliver) direct access to business stakeholders. AI engineers want to see how their model impacts the P&L, not just the F1 score.
• Experimentation Velocity: "We ship to production weekly, not quarterly." This is a massive draw for talent frustrated by bureaucracy.

Crafting the Job Description:

Stop asking for "PhD in Computer Science + 10 years experience in GenAI" (which is impossible).

• Bad: "Must have experience building LLMs from scratch."‍
• Good: "Experience building applications powered by LLMs. innovative problem-solver who loves shipping code."

Speed, Flexibility, and Decision-Making as Competitive Advantages

Big Tech hiring processes are notoriously slow—often taking 6-8 weeks with 5-7 rounds of interviews.

Small GCCs can win by speed:

• The 2-Week Sprint: Aim to go from "First Call" to "Offer Letter" in 10 working days.
• Fewer Rounds, More Signal: Limit interviews to 3 rounds:
  
  1. Screening (Culture + Basics)
  2. Practical Assessment (The Take-Home review)
  3. System Design & Manager Chat‍
• Decisive Action: When you see a good candidate, make an offer immediately. Don't wait to "compare them with 3 others." In the AI market, delay is death

Partnering Smartly to Scale AI Hiring

For a small GCC, building a recruitment engine from scratch is expensive and slow. This is where strategic partnerships come in.

• Build-Operate-Transfer (BOT): If you need a full team of 10+ AI engineers quickly, a BOT model is ideal. A partner hires the team, sets up the infrastructure, and incubates the culture. Once the team is stable (usually 12-18 months), you transfer them to your payroll. This minimizes your initial risk.
• Embedded Recruitment (RPO): Instead of using transactional agencies that throw resumes at the wall, use an "embedded" recruiter who sits with your team (virtually or physically), understands your tech stack, and represents your brand.
• Specialized AI Staffing Firms: Generalist recruiters often can't tell the difference between a Data Analyst and an ML Engineer. Partner with firms that specialize in deep tech hiring to filter candidates before they reach your desk.

Crewscale can do all of this for you, plus more. We take care of talent sourcing, hiring, payroll, compliance, infrastructure, taxation, and everything in between.

Conclusion

The battle for AI talent is not won by the deepest pockets; it is won by the clearest vision.

Small GCCs have a unique advantage in the current market. As engineers grow weary of the "cog in the machine" feeling at massive corporations, the appeal of a tight-knit, high-impact team is stronger than ever. By hiring for applied skills, looking in overlooked places, and moving with agility, your GCC can build an AI team that punches far above its weight.

Looking to build AI capabilities without Big Tech complexity or costs? Partner with Crewscale to design and scale AI teams tailored for your GCC. We specialize in helping small GCCs identify, assess, and onboard applied AI talent that delivers from Day 1.