Core Compensation Benchmarks for AI and ML Engineers in India (2025)
- Vidya Patil
- Dec 15, 2025
- 4 min read
This third blog in the series presents a comprehensive, experience-wise analysis of AI and ML compensation in India. The data shows that AI salaries scale at a faster rate and reach significantly higher ceilings than most other technology roles. As organizations increasingly adopt GenAI, LLM-based architectures, and AI-first product strategies, compensation for specialized talent is accelerating.
This blog explains how salary growth evolves across different experience bands, and why compensation plateaus unless professionals acquire advanced skill depth or transition into strategic roles.*
Why AI Salaries Scale Faster Than Traditional Tech Roles
Artificial Intelligence and Machine Learning roles involve deep specialization, high complexity, and direct business impact. Unlike traditional engineering roles, AI responsibilities include model training, data architecture, performance optimization, deployment workflows, continuous experimentation, and in many cases, applied research.
These capabilities command higher compensation because they generate measurable enterprise outcomes.
These capabilities command higher compensation because they generate measurable enterprise outcomes.
Entry-Level Professionals (0 to 2 Years): Building the Technical Foundation
Entry-level AI and ML roles include job titles such as AI Associate, Junior ML Engineer, Junior Data Scientist, and AI Research Intern.
Research-backed salary ranges are as follows:
General AI Engineer: ₹5 to ₹10 lakh per annum
General ML Engineer: ₹5 to ₹8 lakh per annum
Generative AI Engineer (fresher): up to ₹10 lakh per annum
Exceptional fresh graduates (such as Member of Technical Staff): up to ₹33 lakh per annum
High-growth product companies, deep-tech startups, and research-driven GCCs tend to offer significantly higher entry-level salaries.
The broad variation exists because AI roles differ greatly across industries. High-growth product companies, deep-tech startups, and research-driven GCCs tend to offer significantly higher entry-level salaries than IT services firms. To understand market demand and competitive forces shaping these early career roles, see previous Blog
Mid-Level Professionals (3 to 6 Years): The Critical Growth Stage
This is the stage where AI and ML professionals move beyond foundational learning and begin contributing to complex systems, end-to-end deployments, and production optimization.
Job titles include ML Developer, Data Scientist, AI Engineer, and ML Systems Engineer.
Salary ranges for this segment include:
Mid-level AI Engineer: ₹12 to ₹20 lakh per annum
Mid-level ML Engineer: ₹10 to ₹18 lakh per annum
Overall average salary for professionals with 5 to 9 years of experience: approximately ₹24.5 lakh per annum
Compensation accelerates for candidates with niche specialization in GenAI, Computer Vision, and Deep Learning.
This is also the period where compensation accelerates for candidates with specialization in niche AI fields such as GenAI, Computer Vision, and Deep Learning. Engineers working in high-impact roles can cross ₹20 lakh per annum as early as the fourth year.
Senior and Lead Roles (7 Years and Above): The Leadership and Architecture Premium
Senior AI roles involve ownership of system design, architecture, team leadership, and strategic decision-making. Titles include Lead AI Engineer, MLOps Architect, Principal AI Scientist, and AI Research Lead.
The salary landscape at this level includes:
Senior AI Engineer or Lead: ₹25 to ₹45 lakh per annum or more
Senior ML Engineer or Lead: ₹20 to ₹40 lakh per annum or more
AI and ML Architects: ₹40 to ₹60 lakh per annum
Top-end Architect salaries in leading tech hubs such as Bengaluru or Hyderabad: up to ₹95 lakh per annum, including bonuses and equity
Senior AI talent drives enterprise transformation through system ownership and architectural strategy.
The compensation jump for senior AI talent is driven by the requirement to design full-stack AI ecosystems that integrate data engineering, modelling, deployment, scaling, observability, compliance, and business relevance.
AI Versus ML Engineer Salaries: Understanding the Divergence
Although often grouped together, AI Engineer roles consistently command slightly higher salaries than ML Engineer roles. The reasons include:
AI Engineers typically handle broader system responsibilities beyond modelling.
AI Engineering includes deployment expertise, integration with backend systems, model lifecycle ownership, and scaling infrastructure.
Enterprises are increasingly hiring full-stack AI Engineers instead of pure ML modellers.
The salary ceiling for AI roles is consistently higher across all experience bands.
As a result, the salary ceiling for AI roles is consistently higher across all experience bands.
The Plateau Effect: Why Experience Alone Is Not Enough
Data shows that salary growth stabilizes after seven to ten years unless professionals:
Transition into architecture or principal engineering roles.
Acquire niche deep-tech expertise in GenAI, LLM training, or CV pipelines.
Move into hybrid roles that combine AI expertise with product, strategy, or leadership skills.
AI salaries are no longer tenure-driven. They are skill-driven.
For example, the average salary for professionals with ten to nineteen years of experience is only marginally higher than the average mid-career compensation. This shows that AI salaries are no longer tenure-driven. They are skill-driven.
What the Benchmarks Mean for Professionals and Employers
The compensation benchmarks clearly indicate that AI and ML roles offer accelerated earning potential, but only for those who invest in continuous skill specialization. For employers, the data shows the need to build internal talent pipelines, improve role clarity, and craft career paths that encourage deeper specialization.
The next frontier of compensation growth is domain specialization, not tenure.
The next blog examines the skill premiums for niche AI domains, including Generative AI, Computer Vision, Natural Language Processing, and Deep Learning. These areas now serve as the primary drivers of India’s AI salary inflation.
*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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