AI hiring in India is no longer a niche conversation.
Naukri’s August 2026 JobSpeak report recorded 31% year-on-year growth in AI/ML hiring, while IT and software services hiring grew 11%. The same report showed particularly strong demand for experienced AI/ML professionals: hiring for 8–12 years of experience grew 39%, 13–16 years grew 87%, and the 16+ years segment more than doubled.
That changes the hiring problem.
The question is no longer whether companies can find people who have worked with AI. It is whether they can identify the people who can take an AI system from an interesting prototype to something a business can actually run.
The title is often the least useful part of the brief.
AI Engineer, ML Engineer, Applied Scientist, GenAI Engineer, MLOps Engineer and Data Scientist can describe very different work. One role may be research-heavy. Another may own model deployment. Another may be building retrieval systems, evaluation frameworks or production data pipelines.
A good search starts with ownership.
What will this person build? What will they inherit? What decisions will they own? What does production success look like? Which parts of the stack must they understand deeply enough to challenge existing assumptions?
Those questions create a much better signal than a long list of tools.
The strongest candidates also leave evidence behind. They can explain why a model or architecture was chosen, how they handled constraints, what failed, what changed in production and what they would do differently now. That depth is difficult to measure through keyword matching.
This is where specialist search matters.
AI talent is spread across overlapping communities, companies and role families. A high-volume approach can produce a large list while still missing the people who are genuinely relevant to the problem.
For hiring teams, the practical lesson is simple: define the work before defining the title, and evaluate evidence before counting applications.
The market is growing. The shortlist should still be small.