IT recruitment in India: lateral hiring across services, product companies and GCCs

IT services companies deliver projects for external clients. Their hiring is often driven by client demand and project ramp-ups, which means large requisitions, tight timelines and roles defined around specific client technology stacks. Candidates from services backgrounds frequently have broad exposure across several clients and domains, though the detail of their work may be restricted by client confidentiality.

Product companies build and own their software. They tend to look for depth of ownership — engineers who have designed, shipped and maintained a system over time — and their loops often focus more on design and problem-solving. Global capability centres, the in-house technology and operations hubs of multinational companies, sit between the two: they own long-lived internal platforms and often hire for enterprise technologies, cloud, data and increasingly AI, with processes shaped by their global parent.

Bengaluru remains the deepest pool for product engineering, startups and many GCCs. Hyderabad has grown into a major centre for GCCs and large technology employers. Pune has strong concentrations of services, engineering and automotive-related technology work. Chennai is a long-established centre for IT services, banking technology and SaaS. The NCR region, including Gurugram and Noida, combines services, consulting, fintech and consumer internet companies.

Location still matters even with hybrid work. Many employers now expect some days in the office, and candidates weigh commute, relocation and family considerations heavily. State location and work-mode expectations clearly in the JD, and confirm them with the candidate early — misaligned expectations here are a common and avoidable cause of late-stage drop-off.

A large share of lateral IT demand concentrates in a few families: Java and Spring backend engineers, .NET developers, front-end engineers working in React and TypeScript, full-stack developers, cloud engineers on AWS, Azure or GCP, and DevOps or platform engineers working with CI/CD, containers, Kubernetes and infrastructure as code. Because these stacks are so common, resumes in these areas are also the most keyword-heavy.

The way to separate candidates is to look past the technology list to the work itself. A Java engineer who has maintained a high-traffic transactional service is different from one who has mostly written CRUD APIs. A DevOps engineer who has designed a deployment pipeline and handled production incidents is different from one who has run existing pipelines. Screening for that difference requires reading project descriptions, not counting keywords.

Many Indian IT requisitions are filled partly through staffing vendors, and some vendors source through sub-vendors. That creates familiar problems: the same candidate submitted by two or three vendors, sometimes with slightly different versions of the resume; ownership disputes over who submitted first; and resumes that have been reformatted or rewritten along the way, so that the document you receive is not quite what the candidate wrote.

Clear rules help. Define how candidate ownership is determined, check for duplicates by contact details and work history before review, and where resumes have been rewritten, ask the candidate to confirm the key facts directly. Tracking where each piece of information came from — the candidate, a vendor, or an automated extraction — makes it much easier to resolve discrepancies later.

Compensation in Indian IT is typically discussed as CTC (cost to company) in LPA (lakhs per annum), for example ₹18 LPA. CTC can bundle fixed pay, variable pay, employer provident fund contributions, insurance and other benefits, so two offers with the same headline number can differ meaningfully in take-home pay. Ask candidates for their current fixed and variable split, and present your own offers with the same breakdown to avoid confusion.

Notice periods of 30, 60 or 90 days are common, and some employers allow a buyout where the new employer or the candidate pays for the unserved portion. Whether a buyout is possible depends on the current employer's policy, so confirm it rather than assume it. Counter-offers are also common once a resignation is submitted; the best defence is understanding early why the candidate wants to move, so that you can check whether your offer genuinely addresses it.

A consistent pipeline is what makes IT recruitment measurable. A practical set of stages is: new, reviewed, shortlisted, contacted, interview, assessment, offer and hired. Each stage should have a clear entry condition — for example, a candidate is "reviewed" only when someone has actually looked at their evidence against the requirements, and "shortlisted" only when a recruiter has decided they should be contacted.

Using stages consistently lets you see where candidates stall. If many candidates reach interview but few pass assessment, the JD or screening may be misaligned with the assessment. If many accept offers but few join, the problem is in the notice period, not the interviews. Without consistent stages, those patterns remain invisible.

An accepted offer is not a hire. Between acceptance and joining, candidates may receive counter-offers or competing offers, and some will not join. Track expected joining date, resignation confirmation and actual joining as explicit milestones, and schedule check-ins through the notice period.

When a candidate drops off, record why. Over time, those reasons reveal whether your offers are uncompetitive, your process is too slow, or your notice-period engagement needs work.

RecruitGPT structures IT job descriptions into must-have and good-to-have requirements and structures resumes into projects, roles and skills, so reviewers can see per requirement whether there is evidence found, partial evidence or no evidence yet. Provenance labels — Candidate Provided, AI Extracted, Candidate Confirmed, Verified and Not Verified — show where each fact came from, which is especially useful when resumes have passed through vendor chains. "Verified" is only applied when a human reviewer checked the item; no third-party verification providers are connected.

Recruiters move candidates through stages from new to hired themselves. The AI never auto-rejects, never auto-hires, and does not generate opaque fit scores or infer protected attributes.