Demand For Cloud Operations Talent Hits A New High

There is an alarming crisis unfolding quietly at the heart of India's AI ambitions. According to a joint report by Deloitte India and NASSCOM, AI talent demand in the country is expected to be more than double; from around 600,000 - 650,000 professionals in 2022 to over 1.25 million by 2027. The Indian AI market itself is projected to grow at a staggering 25- 35% CAGR through 2027.

The ambition is real, so is the momentum. But the talent pipeline? That's where the story gets complicated.

For years, cloud infrastructure was treated as the plumbing of enterprise technology, essential but invisible. AI has changed that entirely. Deploying an LLM, running inference pipelines at scale, managing GPU clusters, ensuring uptime for real-time AI applications: none of that happens without robust, well-managed CloudOps. Cloud is no longer the backend. It is business-critical.

NASSCOM’s Digital Enterprise 2025 report found that 74% of enterprises expect AI spending to increase in 2025, while another 20% expect spending to remain at current levels. 27% of enterprises already have AI agents in production or operating at scale, while many more are actively moving beyond pilot programs.

As AI moves from experimentation to execution, infrastructure must scale with it, and someone has to run it.

And that someone is becoming increasingly hard to find.

There is an increasing demand for cloud operations professionals across DevOps, Site Reliability Engineering (SRE) and MLOps, while the supply is still struggling to keep up with this demand. MLOps roles alone are projected to grow 60–80% year-on-year in India through 2025–2026, driven by the sheer volume of AI workloads now moving to cloud platforms. Meanwhile, Gartner estimates that by 2027, 80% of organisations will have incorporated DevOps platforms into their development toolchains, up from just 25% in 2023. The acceleration is real, and it is happening faster than hiring teams can respond.

What’s particularly striking is the nature of the gap. It isn’t just about headcount. The demand-supply disparity for ML Engineers and DevOps Engineers in India already ranges between 60% and 73% across several sectors. The roles exist. The candidates simply are not there.

The education ecosystem itself is beginning to shift in response. Universities are increasingly partnering with training organisations like CloudThat to integrate AI, ML, and cloud certifications directly into degree pathways. Four-year programmes that combine academic curriculum with hands-on cloud and AI training from day one are becoming more common, reflecting how rapidly industry expectations are evolving.

This talent crunch is also reshaping what organisations are looking for. The lines between traditional IT roles are now dissolving. A cloud operations professional today isn't just someone who can provision infrastructure, rather, they are additionally expected to understand AI workloads, manage ML pipelines, interpret observability data and make architectural decisions that can potentially impact model performance and cost.

AI workloads have sharply increased demand for GPU infrastructure, and observability expertise, capabilities that sit at the intersection of cloud, AI, and data engineering. The outcome is a new archetype: the hybrid cloud-AI professional, who combines operational discipline with data fluency. These individuals are rare, highly sought after, and typically fielding multiple offers within days of appearing on the market.

For HR leaders and business heads, this has a clear implication: the talent strategy cannot be built around finding ready-made professionals. It has to be built around creating them.

India's largest IT companies have begun to grasp this. TCS trained 350,000 employees on AI during 2023–24; Wipro trained 220,000. Microsoft committed to AI-skilling 2 million people in India by 2025.

CloudThat itself has trained 1.1 million professionals in Cloud, AI, DevOps, MLOps, and more, across enterprises including TCS, Wipro, and other large organisations. This reflects how enterprise demand for cloud-AI capability is increasingly being addressed through continuous workforce transformation rather than conventional hiring alone.

These are operational necessities. Organisations that don't build cloud-AI competency internally will find themselves perpetually behind, outbid in the market and underpowered in their AI roadmaps.

At the workforce strategy level, the conversation has moved well beyond training catalogues and annual L&D budgets. Upskilling in cloud technologies and AI tools must be embedded into business continuity planning. If your team cannot support your AI initiatives, they will stall, regardless of the quality of your models or the size of your data estate.

There is an optimistic story here, and it is genuinely exciting. India ranks first globally in AI skill penetration, and NASSCOM projects that the country has the capacity to reskill and develop 8–10 million professionals in AI-related services by 2030. The IndiaAI Mission, government-backed skilling initiatives, and a young, trainable workforce all point to structural advantages that few nations can match.

But structural advantages only become competitive advantages when they are acted upon with urgency. The demand for cloud operations talent isn't a future problem. It is a present reality, playing out in hiring freezes, delayed AI deployments, and overextended infrastructure teams across India today.

The enterprises that will lead India's AI decade won't just be the ones with the best tech. The ones who built the operational muscle, the cloud-native, AI-fluent workforce, will be the ones to make that tech actually work.

The runway is short. The opportunity is large.

This article is authored by Bhavesh Goswami founder & CEO, CloudThat.