AI/ML Talent
in India:
2026 Hiring Trends
AI/ML Talent, Skills & Workforce Intelligence
This interactive report maps AI/ML talent in India for 2026: where the talent is, how it moves, which skills and roles employers are hiring for, and what it means for talent acquisition leaders.
Explore the dataDemand is shifting towards professionals who can apply AI/ML skills.
India’s AI/ML talent landscape is expanding rapidly, with the AI workforce growing across major technology hubs. At the same time, AI/ML hiring in India is becoming more concentrated around applied AI, data engineering, cloud and product-focused roles.
Demand is shifting from traditional machine learning profiles towards professionals who can apply AI/ML skills in India to real-world business and technology use cases. Five findings stand out.
Openings are concentrated in metros
The top 10 metros hold 62% of the talent but 87% of the job postings. Bengaluru alone has 15% of the talent and 35% of the openings.
See locationsAI is now a mainstream requirement
About 23% of all job posts ask for AI skills. “AI Engineer” is the fastest-growing title, up 76% year on year.
See rolesProfiles are running ahead of real demand
Many more people list ML and deep learning skills than there are jobs asking for them. That makes rigorous screening more important, not less, for employers building specialised AI talent in India.
See skillsTalent churns quickly
Median tenure is one year, and roughly 1 in 6 professionals changed jobs in the past year (15.5% of the pool).
See mobilityBFSI captive centres keep people longest
BFSI firms average about 12% attrition, versus about 18% at large IT services firms.
See employersAI/ML talent in India: market at a glance
Six indicators describe the pool of AI/ML talent in India: its size, how often people move, how many openings chase it, and who is in it. Hover or focus the i on each indicator for what it means.
Talent pool
What it meansBroad AI/ML and data-adjacent workforce9.44M
Changed jobs in the last year
What it meansHigh mobility15.5%of the pool
Job posts per 100 professionals
What it meansSupply looks large, but qualified supply is thinner2.3
2.3 posts for every 100 professionals
“Engaged” talent (open to approaches)
What it meansMost candidates are passive, so outreach has to be proactive0.6%
Median tenure
What it meansRetention matters as much as acquisition1year
Gender split
What it meansClear room for diversity hiring efforts32%female
32% female68% male
Supply looks large, but qualified supply is thinner.
See the skills dataLocation: supply is spreading, openings are not
Where is AI/ML talent in India concentrated, and where are the openings? Ten metros compared on talent growth, share of the AI/ML talent pool, share of job posts, and job posts per 1,000 professionals. Switch the view to compare supply and demand city by city.
Posts per 1,000 professionals
Job posts for every 1,000 AI/ML professionals in the metro
The chart could not load. The full data is in the table below.
View data table
| Metro | Talent growth (YoY) | Share of talent | Share of job posts | Posts per 1,000 professionals |
|---|---|---|---|---|
| Bengaluru | 22% | 15.1% | 35.4% | 54 |
| Pune | 26% | 4.4% | 8.9% | 46 |
| Mumbai MMR | 25% | 6.9% | 10.8% | 36 |
| Noida | 23% | 2.3% | 3.1% | 31 |
| Hyderabad | 29% | 9.2% | 10.4% | 26 |
| Chennai | 31% | 6.2% | 6.7% | 25 |
| Kolkata | 29% | 2.8% | 2.4% | 19 |
| Delhi | 26% | 10.8% | 7.7% | 16 |
| Ahmedabad | 30% | 1.9% | 0.9% | 11 |
| Coimbatore | 43% | 1.8% | 0.6% | 7 |
Talent share vs job-post share
Where a metro’s share of job posts is larger than its share of talent, openings outweigh available supply.
The chart could not load. The full data is in the table above.
The top 10 metros hold 62% of the talent but 87% of the job postings. Bengaluru alone has 15% of the talent and 35% of the openings.
What this shows
- Bengaluru and Pune are the most competitive markets. They have the most openings for each available professional, which usually means more counter-offers and longer time to hire.
- Bengaluru is growing its pool the slowest of the top 10, at 22%. It keeps up by pulling talent in from elsewhere.
- Delhi, Kolkata, Chennai and Hyderabad have large pools with relatively fewer openings chasing them. These are the realistic places to expand a hiring footprint.
Tier-2 cities are growing fastest from a smaller base
These cities matter for hub-and-spoke delivery models and for hiring at early-career levels.
For organisations building an AI workforce in India, this geographic shift creates an opportunity to look beyond the traditional technology hubs. A broader location strategy can help employers access emerging machine learning talent in India while reducing dependence on the most competitive markets.
Talent migration: Bengaluru attracts talent, the UAE drains it
Talent mobility is becoming an important factor in AI talent acquisition in India. Professionals are moving between established technology hubs, emerging centres and international markets.
Bengaluru
+37K
Listed net inflow: about 37K professionals across the corridors listed. Bengaluru gains from every Indian metro in the dataset.
UAE
−7.7K
Combined net movement: Bengaluru, Delhi, Mumbai, Chennai and Hyderabad together lose a net of about 7.7K professionals to the UAE.
Talent movement corridors: into Bengaluru
Largest net gains. Line width follows the net figure. Not a geographic map.
Talent movement corridors: to the UAE
Combined net outflow. Per-city figures are not reported.
- Hyderabad is a regional magnet. It loses to Bengaluru but gains from Mumbai, Chennai, Delhi and Vijayawada.
- Movement within regions is strong. Delhi loses a lot to Noida, which is movement inside NCR. Chennai gains from Coimbatore and Madurai. Mumbai gains from Pune and Vasai-Virar.
- The UAE is a net drain for every metro. That is the one outbound flow that shows up everywhere.
Takeaway for TA leaders
Hiring outside Bengaluru means recruiting against a steady pull towards Bengaluru. Tighter retention measures in the first year after joining, and relocation-neutral offers, will make a real difference.
Skills: AI demand is real, but concentrated in applied roles
Two views of AI/ML skills in India: which skills are growing fastest in the talent pool, and how much hiring demand exists for each skill relative to the professionals who list it.
Fastest-growing AI skills in the talent pool
Year-on-year growth in profiles listing the skill
The chart could not load. The full data is in the table below.
View data table
| Skill | Growth (YoY) |
|---|---|
| Scikit-learn | +104% |
| Generative AI | +91% |
| AI | +89% |
| Applied ML | +79% |
| Computer Vision | +48% |
| TensorFlow | +48% |
| Predictive Analytics | +48% |
Demand versus supply by skill
Job posts per 1,000 professionals who list the skill
The chart could not load. The full data is in the table below.
View data table
| Skill | Posts per 1,000 | Signal |
|---|---|---|
| Java | 57 | Strong demand |
| Analytical skills | 44 | Strong demand |
| AWS | 42 | Strong demand |
| AI (general) | 36 | Strong and rising |
| SQL | 32 | Solid |
| Python | 26 | Solid, with high volume |
| Kafka / data engineering | 17 | Moderate |
| Machine Learning | 10 | Many more profiles than jobs |
| Generative AI | 7 | Many profiles, targeted demand |
| Deep Learning | 3 | Many more profiles than jobs |
Employers want AI applied inside real products.
Demand is strongest for AI combined with cloud, data engineering and core programming. Pure deep-learning research roles are a small share.
Listed skills are inflating faster than real capability
~1.1M
profiles list deep learning
~3.6K
job posts ask for it
For hiring managers, this means a keyword search alone will surface a lot of noise. Structured technical assessment is what separates real capability from a line on a profile.
Generative AI is where demand is building
Profiles listing it almost doubled in a year, while job posts are still modest. The report expects GenAI to move from a nice-to-have to a stated requirement over the next 12 months.
Roles: AI Engineer is emerging, data roles are established
Demand by role, measured as job posts per 1,000 professionals, alongside year-on-year talent growth where the report provides it.
Demand by role
Job posts per 1,000 professionals
The chart could not load. The full data is in the table below.
Software Engineer and Data Engineer: talent growth is not reported in the source.
View data table
| Role | Talent growth (YoY) | Posts per 1,000 professionals |
|---|---|---|
| Software Engineer | – | 91 |
| Data Engineer | – | 69 |
| AI Engineer | +76% | 52 |
| Full Stack Engineer | +33% | 38 |
| Data Analyst | +30% | 13 |
| Data Specialist | +59% | 101 |
The defining new role
AI Engineer
+76%
talent growth, year on year
52
posts per 1,000 professionals
AI Engineer is the defining new role. It combines fast talent growth with healthy demand.
Early-career pipeline
Talent growth, year on year
The early-career pipeline is growing fast. That makes campus and fresher programmes a viable way to build capacity.
Data Engineer is the pressure point
Demand relative to available talent is among the highest of any core role, because every AI programme needs data pipelines first.
69
Data Engineer posts per 1,000 professionals
Industries: IT services dominates demand, BFSI is a growing competitor for talent
Share of AI/ML job posts, talent growth and the demand rating given in the source, across seven industries.
Share of job posts
Each industry’s share of AI/ML job posts
The chart could not load. The full data is in the table below.
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| Industry | Talent growth (YoY) | Share of job posts | Demand rating in source |
|---|---|---|---|
| IT Services & Consulting | 24% | 47.8% | Very high |
| Business Consulting | 25% | 10.3% | Very high |
| Software Development | 24% | 6.3% | Very high |
| Tech, Info & Media | 30% | 2.8% | Very high |
| Banking | 20% | 2.3% | Moderate |
| Financial Services | 26% | 1.1% | Very high |
| Accounting | 33% | 1.5% | High |
Demand rating in source
As rated in the report
The hidden story is in the talent flows
IT services is a net loser of talent to three sectors. At the same time, it gains talent from software product companies and from education.
IT services net talent loss, by sector
In practice, BFSI and consulting firms are recruiting AI and data talent that IT services trained.
That is useful for BFSI GCCs building teams, and a retention warning for IT services employers.
The result is a more competitive AI talent market in India, where organisations across sectors are competing for overlapping pools of machine learning, data engineering, analytics and emerging GenAI talent.
Employer landscape: consulting grows, IT services plateaus
Established technology employers continue to hold large talent bases, while consulting and specialised employers are expanding their AI/ML capabilities. All figures below are as reported in the source.
AI/ML talent growth by employer group
Year-on-year growth, source-reported
The chart could not load. Deloitte +17%, EY +14%, PwC +14%, Accenture +9%; largest IT services employers 0–1%.
- Big-4 and consulting firms are growing their AI/ML talent fastest. Deloitte grew 17%, EY and PwC 14% each, and Accenture 9%.
- The largest IT services employers are flat. Their AI/ML headcount grew 0–1% year on year, despite having the biggest talent bases.
Attrition, as reported
Approximate figures. Scale 0–30%.
- BFSI captive centres retain best. Attrition is about 9% at the lowest-attrition BFSI captives, against 15–27% across IT services.
- Attrition in IT services averages about 18%, and up to 27% at the highest.
What this means for TA, HR and IT leaders
Six recommendations from the report. Select each one to read the reasoning.
Build Hyderabad, Chennai, Delhi NCR or Coimbatore into your location strategy, where there is more talent per opening.
Hire for AI Engineer, Data Engineer, and GenAI plus cloud profiles, rather than general ML titles.
See PeopleLogic’s AI/ML hiring services
Because listed ML and deep-learning skills far exceed actual capability, structured assessment should be standard.
With a one-year median tenure and a steady pull towards Bengaluru and the UAE, onboarding and early retention need as much budget as hiring.
Intern and trainee pools are growing 45–60% a year. Campus-to-AI-engineer programmes can offset senior talent shortages.
With women at 32% of the pool, specific diversity sourcing is a real differentiator.
What the data means for hiring leaders
A more competitive AI talent market in India, where organisations across sectors are competing for overlapping pools of machine learning, data engineering, analytics and emerging GenAI talent.
From the PeopleLogic 2026 AI/ML Talent report
AI hiring is becoming more application-led
Demand is shifting from traditional machine learning profiles towards professionals who can apply AI/ML skills to real-world business and technology use cases. Employers want AI applied inside real products.
Data engineering remains foundational
Data Engineer is the pressure point. Demand relative to available talent is among the highest of any core role, because every AI programme needs data pipelines first.
GenAI demand is building
Profiles listing Generative AI almost doubled in a year, while job posts are still modest. The report expects GenAI to move from a nice-to-have to a stated requirement over the next 12 months.
Location strategy is becoming more important
Bengaluru and Pune are the most competitive markets. Delhi, Kolkata, Chennai and Hyderabad have large pools with relatively fewer openings chasing them.
Keyword-based sourcing alone is insufficient
Listed skills are inflating faster than real capability. A keyword search alone will surface a lot of noise; structured technical assessment is what separates real capability from a line on a profile.
Retention must be considered alongside acquisition
With a one-year median tenure and a steady pull towards Bengaluru and the UAE, onboarding and early retention need as much budget as hiring.
Emerging cities can expand the talent footprint
Tier-2 cities are growing fastest from a smaller base. They matter for hub-and-spoke delivery models and for hiring at early-career levels.
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