India produces engineering graduates each year, but only a small percentage prepares them for AI, data science and machine learning jobs. As technology evolves every few months, traditional university curricula struggles to maintain the pace. As a result, the gap between what students learn in the classroom and the skills companies expect in the workplace, making education more important than ever before.
“AI is no longer the future. It's already very supportive in the workplace,” said Anshuman Singh, co-founder of Scaler & Scaler School of Technology (SST). “But to stay relevant, experts need to learn to stay one step ahead of AI, dictate it accurately and translate it into actual business value.”
What students really need
To fill the gap, Singh points out three non-negotiable possibilities for students.
Powerful basics of mathematics, problem solving, and data structures.
Access to the latest technologies and practical frameworks.
Learn the agility to continue to adapt as AI evolves.
“Our curriculum is constantly refreshed with input from over 100 industry leaders, including Amazon, Google, and Uber,” added Singh. “This ensures that students not only learn theory, but also work with the very tools that shape the future of their work.”
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The results are already shown. The current first SST cohort in their second year has an internship placement rate of 96.3% across 96 companies, with a role focused primarily on AI in India and overseas. The highest salary reached Rs 2,00,070 per month.
“This shows that the industry's demand is strong for graduates who are not only employable, but also preparing for the future,” Singh said.
Why curriculum gaps continue
In rapidly changing fields like AI, tools evolve within six months, but Indian curricula often takes years to update. A regulatory process designed for stability creates a bottleneck.
“This is where industry integration programs will lead,” Singh explained. “We are not bound by strict structures, so we can update courses multiple times a year in sync with industry changes.”
Many students today have theoretical knowledge, but lack the skills to carry out. “The ability to know is there, but there is no ability to do that,” Singh pointed out.
At SST, students work directly on industry-style projects, from debugging ML models to debugging ML models under tight deadlines to deploying large-scale AI systems. With graduation, they have practical workplace experience.
Make graduates the future – ready
Singh emphasized that shared responsibility is necessary to prepare students for new technology employment.
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Academia needs to integrate industry tools with real projects.
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The industry needs to expand internships, apprenticeships and mentoring.
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Governments can encourage credit recognition in industry-led projects and digital skills training.
“Our programs are co-designed with the industry and taught by practitioners,” Singh said. “By the time students graduate, they have already solved the problems their future employers are working on.”
The skills your employer wants most
Companies consistently demand:
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Running ability on the latest stack.
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Problem solving under constraints.
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Collaboration and communication.
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Adaptability to quickly learn new tools.
“Being able to debug machine learning models and optimize cost and speed pipelines in production is just as valuable as theoretical knowledge,” emphasized Singh.
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Even elite universities are delayed due to slow academic processes. “Academia is designed for stability, not speed,” explained Singh. “SST bypasses this by integrating ongoing industry feedback, so students will graduate from the next breakthrough in AI and even the whole new paradigm.”
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