For decades, an engineering degree followed by an entry-level job in the IT industry has been one of the most dependable pathways into formal white-collar employment. That equation is now being redrawn by artificial intelligence, with companies increasingly automating tasks traditionally assigned to freshers.
As the technology is beginning to reshape the entry-level job market, engineering colleges are responding by changing what they teach. The shift is not simply towards creating more AI engineers, but making students across disciplines capable of using AI in their chosen fields. Colleges are increasingly treating AI as a foundational skill, alongside mathematics and computing, while putting greater emphasis on problem-solving, domain knowledge and hands-on application.
“At Atria University, we think of AI like math,” Praphul Chandra, dean of research and director at the Atria Centre of Excellence in AI, said. “It doesn’t mean that every student should become an AI engineer. The tools will be different and the knowledge required to use those tools will be different.” Atria offers a Minor in AI to students irrespective of their primary degree, allowing, for instance, a life sciences student to pursue AI alongside the core discipline.
The approach is also visible at IIT Madras, which launched a BTech in AI and Data Analytics in 2024 with a strong grounding in linear algebra, probability and optimisation before specialisation. Its Interdisciplinary Dual Degree in Data Science is open to students from different engineering branches.
“The key structural shift needed is moving from siloed coursework to problem-driven, cross-disciplinary learning,” Karthik Raman of the Wadhwani School of Data Science and AI at IIT Madras, said. Students, he said, need strong foundations in mathematics and computing while also learning to apply them to areas such as healthcare, manufacturing and energy. Curricula should also expose students to what AI can and cannot do, even as specific tools evolve rapidly.
This is pushing colleges to move beyond classroom familiarity with AI towards repeated, supervised application. H N Udupa, director of Online Education at SRM University, Sikkim, described the change as a “psychomotor shift”, requiring students to learn through practice rather than passive absorption. The focus, he said, should be on problem-framing, ethical judgement and engaging with intelligent systems across areas ranging from business and defence to space and everyday applications.
At Noida-based Shiv Nadar University, AI is introduced as a unified course in the first year, exposing engineering students to AI/ML, sensors, IoT and 3D modelling through lectures and projects. AI and ML become core courses across all five engineering streams as students specialise, with additional tracks in Applied Machine Intelligence and Foundations of Deep Learning. “We focus on fluency, not familiarity,” Rajeev Kumar Singh, dean and professor at the School of Engineering, said.
The emphasis on application is similarly visible at UPES Dehradun, where undergraduate students work on deploying lightweight models on microcontrollers and IoT-enabled hardware and tackle engineering problems involving computer vision and natural language processing. Its MSc in Statistics and Data Science also incorporates supervised and unsupervised learning, deep learning, probabilistic modelling and advanced data analytics through real-world case-based projects, Prof. Bhaskar Bhatt, head of AI Transformation Office and Dean at School of Design, UPES shared.
The larger change reflects the nature of AI itself. Its development draws on mathematics, biology, psychology, physics, electrical engineering and computer science, while its applications are spreading across healthcare, finance, agriculture, biotech, weather forecasting and marketing. As a result, the value of an engineer may increasingly depend not only on technical proficiency but on the ability to combine AI with knowledge of a specific domain.
The transition in the job market may take time, but the roles that emerge are likely to demand greater ownership, Kashyap Dalal, co-founder and COO of Simplilearn, said. “Not writing code for its own sake but directing systems that write code,” he added, pointing to a workplace where professionals will increasingly orchestrate AI systems rather than perform tasks that can be automated.