The rapid adoption of generative artificial intelligence, automation and AI-led solutions has driven sharp growth among AI startups, but the next phase of scaling is being constrained by a shortage of skilled talent, particularly engineers who can build and deploy production-grade systems.
“The businesses’ expansion has led to a talent supply that does not meet their development needs. Startups encounter greater difficulties than established companies when they attempt to recruit AI specialists because they cannot offer comparable salaries, and their brand presence remains weak,” Kanishk Agrawal, CTO at Judge Group, told Fe.
Global Competition
The supply-demand gap is expected to widen further. According to a report by Bain & Company, India could see more than 2.3 million AI job vacancies by 2027 against an estimated 1.2 million qualified professionals, leaving over one million roles unfilled. “The AI talent crunch is very real. At Oriserve, the challenge isn’t just hiring AI engineers, but finding people who can build production-grade systems for real-world, regulated environments like BFSI,” Anurag Jain, founder and CEO, said. While the firm continues to hire as it scales, AI and systems roles remain the most constrained.
The challenge is not limited to India. Globally, competition for experienced AI talent has intensified. Silicon Valley-based humanoid robotics startup Figure AI reportedly received about 176,000 applications between 2022 and 2025 but hired fewer than 500 people, highlighting the scarcity of suitable candidates. “The demand for skilled AI, ML and LLM professionals is significantly higher than the available supply,” Ankush Sabharwal, founder and CEO of CoRover, said, adding that deployment-ready talent remains limited as the domain continues to evolve.
Industry executives said that the shortage is most acute at the intersection of generative AI, natural language processing and systems engineering, areas critical for voice and enterprise AI applications. Estimates suggest there is roughly one qualified GenAI engineer for every ten open roles, especially in applied AI and MLOps-heavy environments.
Strategic Pivot
To address the gap, startups are increasingly adopting proactive hiring and training strategies. CoRover maintains a rolling hiring pipeline for key AI roles and often onboards talent ahead of demand. Alongside lateral hiring, it is investing in upskilling and cross-skilling existing teams in AI, machine learning and large language models, while using campus partnerships, hackathons and targeted hiring campaigns to identify talent early.
At Oriserve, the focus has shifted to applied problem-solvers rather than generic AI profiles. “We have focused on leverage rather than headcount. This includes investing deeply in internal training, building reusable platforms, and using automation to amplify small teams,” Jain said, adding that the company is prioritising strong foundations over linear expansion.
Gnani AI sees the issue less as a numbers problem and more as a capability challenge. “There is definitely a demand–supply gap for high-quality AI talent, especially in niche areas like speech, language, and applied generative AI,” Ganesh Gopalan, co-founder and CEO, said, adding that stronger screening and internal training have helped offset the impact.
Beyond availability, startups are also contending with rising salary benchmarks and shifting candidate expectations. GenAI and MLOps roles have seen compensation jump by more than 50% in recent years, while many candidates prefer narrowly defined or research-focused work. “However, building enterprise Voice AI requires ownership across reliability, governance, compliance, and scale,” Jain said.
Executives said the combination of limited supply, escalating pay and the need for adaptable, cross-functional engineers is likely to keep hiring challenging in the near term, even as demand for AI solutions continues to grow.