Geographic Salary Variations for AI and ML Engineers in India
- Vidya Patil
- Jan 19
- 4 min read
Geography remains a significant driver of compensation structures in India’s AI and ML job market. While remote and hybrid work models have diversified hiring strategies, major differences continue to persist between India’s leading tech hubs and emerging cities.
These variations are shaped by cost of living, the concentration of high-paying employers, investment patterns by global technology firms, and the maturity of AI ecosystems within each region.
This fifth blog in the AI Compensation Series presents a research-based overview of how AI and ML salaries vary across India’s key locations.
Despite distributed work models, AI compensation in India remains deeply influenced by geography and ecosystem maturity.
Why Geography Still Matters in AI Compensation
Even with hybrid and distributed work becoming mainstream, salary variations across Indian cities remain substantial.
AI engineers in Bengaluru or Delhi NCR often earn more than those in other regions because these cities host the highest concentration of:
Global Capability Centers
Unicorn startups
R&D-heavy technology units
High-paying multinational corporations
Deep-tech research organizations
At the same time, emerging cities such as Pune, Coimbatore, Jaipur, and Ahmedabad are becoming increasingly attractive due to operational cost advantages and a growing talent pool. However, skill scarcity continues to override geography for niche AI roles, especially in Generative AI, LLMs, and Computer Vision.(See Blog 4 for deeper insights)
For niche AI skills, location matters less than capability.
The Tier 1 Pay Triangle: Bengaluru, Delhi NCR, and Hyderabad
India’s three major technology hubs form the core of national AI salary benchmarks. These cities host the majority of global R&D centers, AI labs, and well-funded product startups.
Bengaluru: India’s AI Capital
Bengaluru continues to be the highest-paying and highest-demand location for AI and ML professionals in India.
Key Salary Metrics
General AI and ML salary range: ₹10 to ₹40 lakh per annum
Average base salary for AI engineers: ₹12.1 lakh per annum
Strong presence of global technology giants and R&D centers
Highest concentration of Generative AI and LLM-focused startups
The city’s mature AI ecosystem and deep technical talent base drive its sustained competitiveness.
Bengaluru remains the benchmark city for AI compensation in India.
Delhi NCR and Gurugram: An AI-Driven Corporate Hub
Delhi NCR, including Gurugram and Noida, has surpassed Bengaluru in certain compensation brackets due to strong demand from finance, e-commerce, and enterprise technology sectors.
Key Insights
Salary range: ₹8 to ₹28 lakh per annum
Average base salary for AI engineers: ₹13.08 lakh per annum
High competition from enterprise MNCs, FinTech companies, and global GCCs
Higher compensation for AI roles focused on financial analytics, risk modeling, and fraud detection
In select segments, compensation in Delhi NCR exceeds Bengaluru due to sector-specific demand and multinational presence.
Hyderabad: A Rapidly Rising AI Ecosystem
Hyderabad has emerged as one of India’s fastest-growing AI hubs, supported by expanding GCC footprints and strong government-led innovation initiatives.
Key Metrics
Salary range: ₹8 to ₹32 lakh per annum
Average AI engineer base salary: ₹11.4 lakh per annum
Growing adoption of AI in cloud infrastructure, security, and enterprise data operations
Expansion of global innovation and engineering centers
Hyderabad’s growth positions it as a scalable and cost-efficient alternative to Bengaluru for large-scale AI investments.
Hyderabad is emerging as a serious AI contender with enterprise-grade compensation.
Mumbai: Finance and AI Convergence
Mumbai’s AI compensation landscape is shaped primarily by its dominance in financial services, retail, and enterprise operations.
Salary Benchmarks
Salary range: ₹8 to ₹28 lakh per annum
Average AI base salary: ₹11.4 lakh per annum
High adoption of predictive analytics and AI-driven decision systems in BFSI
Compensation influenced heavily by cost of living and sector specialization
While smaller in scale than Bengaluru or NCR, Mumbai offers strong depth in financial analytics and risk modeling roles.
The Rise of Tier 2 and Tier 3 AI Hubs
Organizations facing wage inflation and infrastructure costs in Tier 1 cities are increasingly expanding into Tier 2 and Tier 3 regions.
Cities such as Pune, Coimbatore, Jaipur, Mohali, and Ahmedabad are becoming meaningful AI clusters.
Key Drivers
Operational costs up to 40 percent lower than US and significantly lower than Tier 1 Indian cities
Expanding engineering talent pools from regional universities
Growth of IndiaAI Labs in Tier 2 and Tier 3 regions
Increased adoption of hybrid and distributed work models
However, despite the lower operational costs, AI and ML salaries in Tier 2 and Tier 3 cities remain competitive due to talent scarcity. For example, Pune maintains a strong salary bracket of ₹8 to ₹25 lakh per annum for AI engineers.
Lower cost does not mean lower compensation for high-impact AI roles
Decentralization of Workforce Strategy
Organizations are shifting from location-specific hiring to capability-based hiring.
Hybrid work models, adopted by nearly 60 percent of organizations, allow employees to live in lower-cost cities while contributing to high-value AI teams distributed across India.
This approach enables:
Broader access to AI talent
Reduced compensation pressure in Tier 1 cities
Greater employee autonomy and flexibility
Wider participation in India’s AI workforce development
Yet, competitive salaries are unavoidable for high-end AI roles anywhere in the country. Location no longer limits compensation for niche generative AI, computer vision, or deep learning roles.
This shift reflects broader AI compensation trends discussed earlier in the series.
How Geography Influences AI Compensation
While Tier 1 cities still define AI salary benchmarks, India’s AI talent ecosystem is becoming more geographically distributed. Salaries in Tier 2 and Tier 3 cities remain competitive because niche AI skills command uniform nationwide demand.
In the next blog, we dive into the structure of total rewards, including the split between base and variable pay, the use of RSUs and ESOPs, and the role of non-monetary benefits in attracting and retaining AI talent.
In AI compensation, capability now travels faster than geography.
*Disclaimer: The compensation data and insights shared in this article are based on internal research, market observations, and industry references. Detailed references can be shared upon request.
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