The Great Divide: A Tale of Two Tiers
On the surface, the numbers for India's AI job market look optimistic. A recent report from Nomura highlights that for every job lost to AI, roughly 2.6 new AI-related roles are created. Between 2022 and August 2026, India added over 83,000 AI jobs while
losing around 32,000. But beneath this positive net gain lies a more complex reality: the market is splitting into two distinct, unequal tiers. The top tier is a high-stakes arena for seasoned professionals—those with 7-10 years of experience, specialised skills in Generative AI, MLOps, or AI system architecture. These are the leaders companies are desperately seeking to drive AI adoption, manage complex projects, and deliver tangible business outcomes. The second tier is a crowded, competitive space for freshers and workers with only basic or generic AI certifications. Here, demand is weaker and the traditional career ladder, where graduates learned on the job, is being dismantled by automation.
The Experience Premium: Why Companies Are Paying More for Less
The demand chasm is directly reflected in compensation. While an entry-level AI engineer might start between ₹6-9 lakh per annum, a senior professional with a decade of experience and specialised skills can command salaries upwards of ₹30-60 lakh. Some principal or architect roles at Global Capability Centres (GCCs) or product companies push compensation even higher. This isn't just a standard experience-based pay gap; it's a strategic premium. Companies are no longer just hiring for AI knowledge; they are hiring for proven application and impact. The logic is simple: automation is efficiently handling many routine, entry-level tasks like basic coding, data processing, and customer support queries. As a result, firms are reducing mass campus recruitment for these roles. Instead, they are consolidating their investment in a smaller number of high-impact senior leaders who can manage AI systems, ensure reliability, and guide teams, effectively doing the work that once required a larger, less experienced workforce.
The Shifting Conversation: From 'Shortage' to 'Mismatch'
This two-tier structure fundamentally changes the conversation around AI talent in India. For years, the dominant theme was a simple 'talent shortage'. Now, the problem is more accurately described as a 'talent mismatch'. There is a critical shortage of experienced, production-ready AI engineers, but a potential oversupply of candidates with theoretical knowledge or basic certifications. Displaced workers from roles like customer support are rarely able to transition into high-end AI engineering positions, creating what Nomura calls “distributional pain”. This shift disrupts the traditional career pathway for Indian graduates, where entry-level IT services roles served as a crucial training ground. The new expectation for freshers is to arrive with demonstrable, hands-on project experience and the ability to solve real-world problems from day one.
Navigating the New Reality
For job seekers, navigating this landscape requires a new strategy. For freshers and those in the lower tier, the path forward is through specialisation and portfolio-building. Generic 'AI literacy' is no longer enough. Gaining hands-on experience with in-demand skills like Generative AI, cloud platforms, and MLOps through live projects and internships is critical to stand out. For experienced professionals, the opportunity is immense, but so is the need for continuous upskilling. A recent study found that senior professionals are now the single largest group enrolling in AI courses, signalling that even those at the top recognise the need to stay current. For companies, the challenge is twofold. While hiring elite talent is a short-term fix, long-term success will depend on building internal pipelines to bridge the gap between the two tiers. Without pathways for junior talent to grow into senior roles, firms risk creating a permanent leadership vacuum.
