How can a GCC scale specialised engineering teams in India?

Scaling a specialised team is a different problem from scaling a general one. A centre that needs forty more Java developers draws on a large market and a well-understood way of testing them. A centre that needs six MLOps engineers, two AI infrastructure specialists and a data science lead is competing for a much smaller pool, and the people who can judge those candidates are usually the busiest engineers in the building.

What works is to scale one role at a time, with the definition, the pipeline and the assessment in place before the requisitions pile up.

Why is scaling specialised roles harder than adding headcount?

India’s GCC sector is large and still growing. The Nasscom and Zinnov GCC Value Orbit report counts about 2,117 GCCs employing around 2.36 million professionals as of FY26, with more than 1,200 of those centres reporting embedded AI and machine learning capabilities. The same report puts India’s AI talent base at about 250,000 professionals [1]. Many centres are looking for overlapping skills at the same time.

The report also notes that organisations are prioritising redeployment and AI-led productivity over linear headcount growth [1]. In practice, many Site Leads are asked to add capability without a matching increase in headcount, so each specialist hire carries more weight.

Speed is a recurring problem. In a 2026 survey of more than 150 GCC leaders and HR executives, commissioned by the recruitment software company Ceipal with People Matters, 58% said they take more than 45 days to fill critical roles, and 27% said more than 60 days [2]. Vendor-commissioned figures deserve some caution, but the pattern will be familiar to most talent heads.

What does “role by role” mean in practice?

Each specialist role gets its own definition, its own sourcing plan and its own assessment, rather than sitting in a general hiring queue. Four steps cover most of it.

1. Define the role as outcomes and skills

A job description lists tools. A role definition says what the person must deliver in their first six to twelve months, and which few skills that depends on. “Experience with Kubernetes and MLflow” is a tool list. “Take three models from notebook to a monitored production service, with rollback, within two quarters” is an outcome. It tells you to test for deployment, monitoring and systems design, not familiarity with a product name.

Keep the list to four to six skills, and agree a pass mark with the hiring manager before the first CV arrives. If the manager and the talent team cannot agree what good looks like, more sourcing will not fix it.

2. Build supply before demand peaks

Most delays happen because sourcing starts on the day the requisition opens. Once a capability is established, specialist roles tend to recur, so keep a warm pipeline for them: people already screened against your definition, whose interest and availability you check regularly.

Look at adjacent profiles as well. A strong backend engineer who has built data pipelines may become an excellent MLOps engineer with the right support, and your own team is often the best place to find those people first.

3. Have practising engineers assess against the definition

Keyword screening fails for specialist roles because the vocabulary is shared by people with very different depth. Someone who has run a model in production for two years and someone who has completed a course use the same words on a CV. The difference shows only when an engineer who does the work asks about trade-offs, failures and what the candidate would do differently.

Your senior engineers are the best judges and the scarcest resource. Protect their time with structured screening in front of them, so they spend interviews on the final few candidates rather than the first fifty.

4. Plan the path from flexible to permanent

When a capability is new, a flexible model (contract, augmentation or a fixed-term arrangement) lets you start before every role is approved and test fit on real work. Decide early how and when those people can move onto your payroll, what that will cost, and who makes the call. A clear path keeps good people, avoids surprise fees later, and gives your leadership a cleaner story: the centre is building its own capability, not renting it indefinitely.

A checklist for each specialist role

How do you explain the plan to global leadership?

Site Leads are usually asked two things: how fast, and how sure. Answer both with evidence. Report the time from brief to first qualified candidate for each role, the share of candidates who pass assessment, and how hires perform against the outcomes they were hired for. A short written rationale for each senior hire, setting out why this person passed and what risk remains, is far easier to defend than “the panel liked them”.

Keep ownership visible. Whether you hire directly or with outside help, your managers should own the role definition and the final decision. Outside support should strengthen that process, not replace it.

How Viamagus select approaches it

We work role by role in the same way. Each brief becomes a scorecard of five skills with a pass mark, engineers who build and run production AI assess every candidate, and you receive written verdicts rather than a stack of CVs. For ten specialist roles we keep a bench of 10+ vetted people per role, refreshed weekly, and people placed through resource augmentation can move onto your payroll from month 13 with no conversion fee. See how it works, or use the business case notes when you take the plan to your leadership.