AI and Machine Learning careers in India are expanding quickly in 2026, and compensation is moving up as companies adopt intelligent systems across products, operations, and engineering workflows. If you’re planning your next role—or mapping a long-term path—salary clarity helps you set realistic expectations and focus on the skills that actually improve your offers.
Salary ranges by experience (what the market typically looks like)
A simple way to understand pay is to break it down by experience band:
Freshers (0–2 years): AI Engineer compensation often sits around ₹6–₹9 LPA, while ML Engineer roles commonly fall around ₹5–₹8 LPA. These roles usually include titles like AI/ML Associate or Junior Engineer.
Mid-level (3–6 years): AI Engineer salaries frequently land around ₹12–₹20 LPA, and ML Engineer roles around ₹10–₹18 LPA. This is where specialization starts to matter—model development, data pipelines, or applied AI for business teams.
Senior (7+ years): Senior AI Engineer roles can reach ₹25–₹45 LPA+, and senior ML Engineer roles can reach ₹20–₹40 LPA+, often tied to leadership ownership, architecture decisions, and deployment maturity.
There’s also a noticeable premium for global remote roles, where senior engineers working for overseas firms can earn ₹60–₹80 LPA equivalent depending on scope and contract structure.
City-wise differences (why location still affects offers)
Even with remote work growth, major tech hubs still pay differently. A city-wise snapshot shows ranges such as:
Bengaluru: ₹15–₹40 LPA
Hyderabad: ₹12–₹32 LPA
Pune/Mumbai: ₹10–₹30 LPA
Chennai/Coimbatore: ₹9–₹25 LPA
Delhi NCR: ₹10–₹28 LPA
Use location ranges as a benchmark—not a limit. You can often negotiate above the typical band when your role includes production responsibility (deployment, monitoring, reliability), not just experimentation.
What increases salary faster than “years of experience”
Compensation growth is usually tied to impact, not time. The strongest drivers include:
Skill stack depth: Employers value engineers who can deliver end-to-end results. Skills like Python, TensorFlow/PyTorch, LangChain, and MLOps are highlighted as pay boosters, with the article noting that this expertise can lift compensation meaningfully (often framed as 20–30% impact depending on role).
Domain + business relevance: Applied AI in areas like fintech or healthcare can pay more because the systems are high-stakes and require strong quality controls.
Proof of work: A credible portfolio (projects, deployments, measurable results) tends to outperform generic claims.
Deployment maturity: Knowing how to ship models—versioning, monitoring, rollback, data drift checks—often pushes candidates into higher bands.
A smart “transition” path (for testers and automation engineers too)
One growing route is the intersection of AI with engineering productivity. The same breakdown discusses AI in software testing and notes that professionals who blend automation skills with AI can move into AI testing roles with salaries around ₹12–₹22 LPA after upskilling.
If you want the full table view (experience, city bands, and skill impact) in one place, learn more to get full experience: