India's GenAI boom hits talent wall with 83% skills gap: Quess Corp

Demand shifts from AI experimentation to deployment, creating acute shortages in GenAI, governance, MLOps and other specialist roles

India faces an 82.9 per cent shortage in generative artificial intelligence (GenAI) skills despite having an AI workforce of around 920,000 professionals, according to a report by staffing firm Quess Corp released on Wednesday.

 

The report said demand for specialised capabilities such as GenAI, AI deployment engineering, governance, and machine learning operations (MLOps) is rising rapidly as companies move from AI experimentation to production-scale deployment.

 

The study, based on 350,000 AI-related job postings over the past 90 days, found that GenAI recorded the widest talent gap, followed by AI deployment engineering (72.4 per cent), AI governance (70 per cent), and MLOps (68 per cent). While India remains one of the world’s largest AI talent markets, the report said the biggest shortages are emerging in skills needed to deploy, operate, and govern AI systems at scale.

 

“Foundational ML and decision intelligence are reasonably staffed. GenAI, AI model operations, AI deployment engineering, natural language processing (NLP), AI governance, and AI security all show critical shortages,” the report observed.

 

AI security roles showed a 67 per cent shortage, while NLP roles had a shortage of 63 per cent. Meanwhile, hiring demand for foundational AI skills remains relatively balanced, with talent shortages considerably lower in areas such as ML (29 per cent) and decision intelligence (17 per cent).

 

The findings suggest that the challenge for employers is no longer access to AI talent in general, but the availability of specialised professionals capable of building production-ready systems and integrating them into enterprise workflows.

 

According to the report, India has a sizeable pool of professionals with foundational AI and analytical capabilities, but worryingly, fewer workers possess expertise in areas such as model deployment, governance frameworks, observability, security, and enterprise-scale AI implementation.

 

The report estimated that India currently has around 920,000 AI professionals across core AI and AI-embedded roles. However, demand is increasingly concentrated in specialist functions.

 

Nearly 68 per cent of AI hiring demand is now directed towards core AI roles, while a large share of the available workforce is concentrated in AI-adjacent positions that support implementation and business transformation, the report said. Rather than focusing on experimentation and proof-of-concept projects, organisations are increasingly looking to deploy AI systems in customer service, software development, operations, marketing, compliance, and decision-making functions, it added.

 

The report also highlighted the emergence of new roles linked to agentic AI systems and enterprise AI deployment. Hiring demand for positions such as agentic AI developer, GenAI engineer, AI platform engineer, and AI product owner has grown sharply over the past year as companies seek to operationalise AI across business functions. “In corporate and advisory functions, AI demand is moving into judgement-heavy work. These job descriptions are not asking for model builders; they are asking for functional specialists who can use AI to improve decision quality, control discipline, and workforce capability,” said the report.

 

At the same time, employers continue to face acute shortages in mid-career talent. The report found that nearly half of all AI hiring demand is concentrated among professionals with three to five years of experience, creating intense competition for workers who combine technical expertise with practical deployment experience.

 

According to Quess Corp, India is expected to create more than 2.3 million AI-embedded roles by 2030 while transforming over 4.5 million existing jobs. The report said future workforce demand is likely to be driven less by traditional AI research roles and more by professionals who can implement, govern, and scale AI systems in real-world business environments.