AI tools can take several days to learn. But building the next electric car batteries, semiconductor chips, and industrial robots will require years of science and engineering understanding. That is the challenge facing India’s technology talent. As artificial intelligence changes the way companies operate, recruiters are asking bigger questions. Are engineers so focused on software skills that they forget the fundamentals of driving innovation?
For decades, India’s engineering success story has been built around software. Coding has become a gateway to technology careers, fueling the growth of IT services and creating a workforce of millions. With more than 1 million engineering graduates entering the ecosystem each year, programming skills have become the most sought-after and most employable competency.
However, in the AI era, the values that companies seek from engineers are changing. The question is no longer just whether graduates can write code. What matters is whether you understand the problem your code is trying to solve. “AI is changing the way work is done, but it is not changing the foundation of good engineering,” said Sabine Schneider, Executive Vice President and Head of Human Resources and Organization, Siemens India.
This shift is visible as technology moves beyond software applications and into the physical world. Electric vehicles, semiconductors, robotics, industrial automation, and advanced manufacturing require engineers who understand how systems work, from materials and energy to mechanical processes and electronics. Batteries aren’t just a software issue. Semiconductors are not just a coding problem. Robots are more than just algorithms. The science behind these technologies is important.
For years, students have been drawn to software. Because software offers a clear career path. As coding skills dominate engineering education and recruitment, subjects such as physics, chemistry, and advanced mathematics were often left on the back burner.
mathematical rigor
The rise of AI is now forcing us to reconsider. Companies say the next generation of engineers will need to combine software functionality with deeper domain understanding. “Software should be a layer built on top of the foundation, not a replacement for it,” said Vivek Ranjan, CHRO at Zensar.
The rise of AI itself has brought mathematics back into the spotlight. Concepts such as linear algebra, probability, statistics, and optimization, once considered theoretical subjects, are now at the core of modern AI systems. According to Ranjan, linear algebra is the mother language of machine learning. Large-scale language models and machine learning applications are built on these foundations. Engineers who understand the mathematics behind AI can do more than use existing tools. Improve models, identify limitations, and create specialized solutions.
This change is also changing what recruiters are looking for in new graduates. Coding ability remains important, but companies are increasingly valuing problem solving, curiosity, systems thinking, and the ability to apply first principles. Sandhya Arun, Wipro’s global chief technology officer, said the AI revolution is creating opportunities across semiconductors, energy, materials research and advanced manufacturing, but these areas require deeper scientific understanding. “As AI expands what engineers can build, it also increases the complexity of systems and the rigor required to design, validate, and optimize solutions,” she says.
Accenture’s view reflects this changing demand. Aditi Kulkarni, leader of the Global Network and Center for Advanced Technology in India, says the workforce of the future will need to combine scientific knowledge with practical technology skills. “The real differentiator is people who have a deep foundation in science and engineering, combined with specialized knowledge and fluency in AI,” she says.
Demand for these skills can be seen across all industries. Electric mobility requires engineers to understand battery chemistry, thermal management, and energy efficiency. In semiconductors, physics and materials science are important. Robotics requires mechanical systems, sensors, and software to work together precisely.
Physics, once considered primarily an academic foundation, is once again becoming a practical technology skill. “Physics is the backbone of these fields,” Ranjan said, highlighting the role of physics in fields ranging from EV batteries to semiconductor design.
turning point of talent
However, the challenge is not just technical knowledge. Companies are also looking for engineers who can communicate, collaborate, and continuously learn. The speed at which technology changes means that today’s skills can quickly become obsolete.
Suki Sudarshan, Director of Global Leadership Talent Acquisition at EY GDS, points out that the gap between AI users and AI builders is widening. Although many graduates are comfortable using technology tools, fewer demonstrate the ability to solve complex problems, understand systems, and innovate.
For India, this opportunity is important. The country is large, has a strong tech talent pool and a growing startup ecosystem. But to become a deep tech powerhouse, we need to move beyond technology adoption to technology creation. That path depends on stronger research capabilities, closer industry-academia collaboration, and an education system that fosters curiosity, experimentation, and a deep understanding of science.
Successful engineers in the AI era will not just know the latest tools, but will be able to combine technology with the ability to think from first principles. Sandhya Arun says: “The future is not just about creating software for the world, but about solving the most complex challenges through mastery of science, technology, and a human-centered mind.”
