India is routinely described as an AI talent superpower, and on several measures the description holds. The Stanford AI Index 2025 found that India recorded the highest relative AI hiring growth of any country in its sample in 2024, at 33.4%, ranked second only to the United States on AI skill penetration over 2015 to 2024, and showed the largest increase in AI talent concentration between 2016 and 2024 of any country, at 252%. India also accounted for 19.9% of AI contributions on GitHub, second in the world. Yet founders and chief technology officers in India report, with equal consistency, that they cannot find the people they need. Both statements are true, and reconciling them is the purpose of this piece.
The reconciliation lies in depth. AI talent in India is abundant at the level of people who use AI tools and build applications on top of existing models, adequate at the level of engineers who can put models into reliable production, and scarce at the level of researchers who train frontier models and engineers who run large-scale training infrastructure. Headline numbers mix all three, and a hiring plan that treats them as one pool will fail.
This piece is for CTOs, CHROs, investors and policymakers. Its original contribution is a talent supply map that classifies AI roles by depth and assesses where in the Indian economy each kind of talent currently sits, using Stanford AI Index, Nasscom, Nasscom-Zinnov and LinkedIn data. The supply ratings are the author's judgments, and the underlying figures come from different methodologies that are not additive.
What the headline data says about AI talent in India
Four datasets together give a reasonably clear picture.
Stanford AI Index 2025. The Economy chapter, which draws on LinkedIn data, is the source of the hiring, skill penetration and concentration figures above. It adds two details that matter. India's relative AI skill penetration rate for women, 1.9, was the highest among the countries listed, ahead of the United States at 1.7. And the report cautions that LinkedIn covers a lower share of the labour force in India than in some other countries, so the India figures need extra care. On capital, the Index's Quid data show India with about $1.16 billion of private AI investment in 2024 and about $11.29 billion cumulatively from 2013 to 2024, and 74 newly funded AI companies in 2024. Talent is plentiful relative to the capital that employs it in Indian AI companies, which is one reason so much of it works for foreign employers.
Nasscom. Nasscom's Strategic Review of February 2026, as reported by ETV Bharat, put the technology industry's direct workforce at 59.5 lakh at the end of FY26. Of these, more than 20 lakh had been upskilled in AI, including up to 3 lakh in advanced AI skills. A Nasscom and Deloitte report of August 2024 projected AI talent demand in India rising from 6 to 6.5 lakh professionals to more than 12.5 lakh over 2022 to 2027, which it described as potentially signalling a demand-supply gap.
Nasscom-Zinnov on global capability centres. The GCC landscape report released on 3 July 2026 counted 2,117 GCCs in India with about 2.36 million professionals, including 250,000 AI professionals. More than 1,200 GCCs had embedded AI and machine learning capabilities, supported by more than 250 dedicated centres of excellence, and nearly half of the GCCs set up since FY2021 were built with AI as a core focus from the start.
LinkedIn. A LinkedIn report covered by IANS in April 2026 found AI engineering job postings in India up 59.5% year on year, faster than in the US, UK, France or Germany, with Hyderabad up 51% and Vijayawada up 45.5%. It also reported that AI engineering talent in Indian manufacturing grew fourfold to reach 2% of that workforce in 2025.
These numbers do not sum. The GCC AI professionals are very likely included in Nasscom's industry headcount, and LinkedIn's measures depend on how members describe themselves. Read as indicators rather than counts, however, they point the same way: a very large base of AI-literate engineers, a fast- growing middle layer, and a rapidly rising demand curve.
Where the talent sits
The more useful question for an employer is not how many but where. Five pools hold most of India's AI-capable engineers, and each has a different profile.
IT services firms. The largest pool by volume. Most of Nasscom's 20 lakh upskilled employees sit here. Their strength is integrating AI into enterprise systems at scale; their exposure to training models from scratch is limited, because client work rarely requires it.
Global capability centres. The fastest-deepening pool. The Nasscom-Zinnov figure of 250,000 AI professionals, and the share of new GCCs designed around AI, suggest that much of India's production machine learning experience is now accumulating inside captive centres of multinational companies, working on those companies' own products and data. GCCs compete hard for senior engineers and can pay global-benchmarked salaries.
Product companies and startups. A smaller pool with the broadest roles. The editor-verified Nasscom-Zinnov start-up report counts more than 4,200 deeptech startups, with AI attracting 91% of deeptech funding in 2025. Engineers here often cover the whole stack from data pipeline to user interface, which makes them valuable and hard to retain.
Academia and public research. The deepest research talent but the thinnest pool. India's foundation- model efforts under the IndiaAI Mission, including the 12 startups the PIB backgrounder lists as selected for foundation models, draw heavily on it.
The diaspora. A pool outside India that matters to it. Press coverage of the 2025 AI Index, including a report in tele.net, described a small net outflow of AI professionals from India on LinkedIn's migration measure. The author could not read the exact value from the Index's published chart, and the figure should be treated as indicative. What is clear is that a meaningful share of India-trained senior researchers work abroad, and that returning talent is a recruitment channel in its own right.
A talent supply map by role and depth
The table below classifies AI roles into four depth tiers and assigns a supply rating for India based on the data above. It is an original framework; the supply ratings are qualitative judgments, not measurements.
Running across all tiers are two cross-cutting roles that are scarce at every level: AI product managers who can translate a business problem into a measurable model objective, and AI governance and risk specialists who understand both the technology and Indian regulation. Neither shows up clearly in the headline data, and both are frequent bottlenecks in practice.
Two further patterns in the data deserve attention because they cut against common assumptions. The first is geography. The LinkedIn figures for Hyderabad and Vijayawada suggest that AI hiring is no longer confined to Bengaluru and the National Capital Region, and that employers willing to build teams in fast-growing secondary cities may face less competition for tier 2 and tier 3 engineers. The second is gender. India's relative AI skill penetration rate for women was the highest among the countries the Stanford Index lists, and one passage of the Index names India as one of only two countries where AI talent concentration was not higher among men than among women, although another passage of the same chapter states that men outnumber women in AI roles in every country sampled, so that second point should be read cautiously. Even on the conservative reading, an employer whose AI hiring funnel is heavily male is therefore not reflecting the Indian talent pool; it is filtering it. A separate PanScience Research piece in this cluster on AI recruitment covers how screening tools can reinforce that kind of filtering.
What the map implies for employers
The map suggests three hiring strategies, one for each kind of organisation.
Enterprises adopting AI mostly need tier 2 talent and a small amount of tier 3. Their best source is often internal: engineers already upskilled to tier 1 can be developed into tier 2 builders more quickly than outside hires can learn the company's systems and data. A separate PanScience Research piece on AI operating models covers how to organise such teams.
Startups building AI products need a few tier 3 engineers early, because the gap between a demonstration and a dependable product is mainly a production engineering gap. They compete with GCCs for those people and usually cannot match GCC salaries; their offer has to rest on scope, ownership and equity. A separate PanScience Research piece in this cluster on building the first AI team covers role sequencing.
Foundation-model efforts need tier 4 talent that India has in small numbers. For them, compute access through the IndiaAI Mission, which the PIB backgrounder describes as more than 38,000 GPUs at about ₹65 per hour, is necessary but not sufficient. The constraint is people who have run large training jobs before, and diaspora recruitment and partnerships with academic groups are the realistic routes.
A practice example: shared engineering
One way smaller organisations address tier 3 scarcity is to share it. PanScience Innovations reports running shared in-house product, technology, GTM, operations, finance and legal functions across more than 24 co-founded ventures, eight of them in AI infrastructure; these figures are company- reported. The logic is that a production ML or platform engineer whom no single early-stage venture could justify full time can serve several, and that patterns built once, for deployment, monitoring or security, can be reused. The trade-off is contention: when two ventures need the same senior engineer in the same week, someone waits. A separate PanScience Research piece on the venture studio model covers how shared functions are governed.
The broader principle applies outside venture studios. Groups of companies, industry consortia and even large enterprises with many business units can pool scarce tier 3 and tier 4 talent in a central platform team while keeping tier 2 builders close to each product.
What policymakers should watch
The data suggests that India's AI talent challenge is shifting from volume to depth. Upskilling programmes have been effective at producing tier 1 and tier 2 capability. The harder problem is tier 4, where the binding constraints are research funding, doctoral pipelines, access to large compute for academic groups, and the career incentives that keep researchers in India. The Stanford data showing India's private AI investment at about a fortieth of the United States' cumulative total, measured against India's second-place rank in skill penetration, is the clearest single statement of the imbalance: India trains far more AI talent than its own AI companies currently fund.
Two indicators are worth tracking each year: the share of GCC AI roles that involve owning models rather than integrating them, and the net migration of senior AI researchers. Movement in either would signal that depth, not just breadth, is improving.
Conclusion
AI talent in India is plentiful where the work is applying models and scarce where the work is training and running them at scale. The Stanford, Nasscom and LinkedIn data agree on the breadth, and they agree that the middle layer is growing quickly, much of it inside GCCs. The talent supply map turns those figures into a hiring guide: develop tier 2 builders internally, compete for tier 3 production engineers on scope rather than salary or share them across teams, and treat tier 4 research talent as a strategic recruitment effort that includes the diaspora. Employers who know which tier they are hiring for will stop being surprised that a country with so much AI talent can be so hard to hire in.
Methodology and sources
This article reads several datasets against one another and adds an original classification, the talent supply map, whose supply ratings are the author's judgments. Stanford AI Index 2025 figures on AI hiring, skill penetration, talent concentration, investment and newly funded companies were read from the Economy chapter PDF (hai.stanford.edu/assets/files/hai_ai-index-report-2025_chapter4_final.pdf); the GitHub share is from the editor-verified list. The net migration description relies on tele.net's coverage of the Index (tele.net.in/india-tops-in-ai-hiring-but-trails-in-talent-retention-ip-and- investment-stanford-ai-index), because the author could not read the value directly from the chart. Workforce and upskilling figures are from ETV Bharat's report of Nasscom's Strategic Review, 24 February 2026 (etvbharat.com/en/business/india-fy26-tech-revenues-hit-usd-315-billion- enn26022404879). The demand projection is from Deloitte India's press release of 20 August 2024 on the Nasscom-Deloitte report "Advancing India's AI Skills" (deloitte.com/in/en/about/press- room/bridging-the-ai-talent-gap-to-boost-indias-tech-and-economic-impact-deloitte-nasscom- report.html). GCC figures are from the Nasscom-Zinnov report as carried by The Tribune on 3 July 2026 (tribuneindia.com/news/business/india-s-gccs-are-increasingly-leading-the-ai-mandate-for- global-enterprises-driving-global-value-creation-nasscom-zinnov-report); that article is a syndicated release. LinkedIn AI engineering figures are from IANS, 24 April 2026 (ianslive.in/ai-engineering- hiring-in-india-jumps-595-pc-as-demand-spreads-beyond-metros--20260424121646). Deeptech start- up and IndiaAI Mission figures are from the editor-verified list (Nasscom-Zinnov Indian Tech Start-up Report 2025; PIB IndiaAI backgrounder, February 2026). PanScience figures are company-reported, from panscience.xyz. The datasets use different methods and overlap; they should not be added together.