‘Core Engineering Fundamentals Matter’: What Engineers Need To Know In 2026 To Stay Relevant In AI Era

India produces roughly 1.5 million engineering graduates every year, but only fewer than 20% of them are considered readily employable in core engineering roles, according to several studies, including NASSCOM’s. As Artificial Intelligence (AI) reshapes the workplace, the gap between what engineering colleges teach and what employers expect is becoming harder to ignore.

An engineering degree still provides the mathematical, scientific and technical foundations that the profession depends on. But AI is changing how those foundations are applied — from coding and testing to simulation, design and data analysis. Reports suggest that India’s engineering employability rate stands at about 72% in 2025, yet only a small percentage of graduates find

jobs aligned with their core disciplines.

Does this mean an engineering degree still enough, or does an engineer now need an entirely different skill set to remain relevant?

“The workplace has moved much faster than most degree programme and curriculum. Teams now work with huge volumes of data, make decisions in near real-time, and deliver in shorter cycles with far less tolerance for defects, security gaps, or compliance misses. Engineers are also expected to contribute from day one…AI has amplified this shift, and raises the baseline expectation at work. A four-year degree still gives you the fundamentals, but it cannot keep pace with how quickly tools, platforms, and delivery practices evolve,” said Prasad Medury, Chairman, India, Odgers.

In India’s Global Capability Centre (GCC) sector, nearly two-thirds of new roles created this year — 64% — are projected to require AI, data science or intelligent-automation skills. GCC hiring is itself expected to reach 510,452 jobs this year.

How Much Has The Engineering Job Changed?

AI is now taking over or assisting with some of the routine work that traditionally formed the early stages of an engineer’s career.

For example, software, coding, testing and debugging, analysing data and automating repetitive development tasks are being done through AI. That does not mean that software engineering job is disappearing. The US Bureau of Labor Statistics projections cited by Business Insider show employment for software developers is expected to rise by about 175,000 between 2025 and 2035, reaching roughly 2 million workers. The reason is partly the continued demand for software in AI, IoT, robotics, automation, cybersecurity and connected products.

The difference is in what the engineer is being asked to do. “AI has compressed the grunt work. Software developers can draft codes in minutes, while design engineers can automate simulations and explore multiple options much faster. Core engineering hasn’t changed, but where engineers spend their time has. The focus is shifting from manual execution to problem formulation, reviewing AI-generated outputs, and making system-level decisions. AI won’t replace engineers; the real risk is that engineers who ignore AI will be left behind by those who learn to use it effectively,” said Associate Professor Monali Mavani, Department of Computer Science and Information Systems, BITS Pilani WILP.

This shift is already creating new hybrid roles. LinkedIn’s latest look at India’s AI job market highlights the rise of the Forward Deployed Engineer, a role that sits between software engineering, AI, product development and customer-facing problem-solving. Tata Consultancy Services (TCS) plans to hire 8,900 such engineers this year, while Infosys is targeting up to 6,000 and Wipro plans to deploy 10,000 over the next 18 months.

The point is not that every engineer needs to become an FDE. It is that the boundary between engineering, product, business and AI is becoming less rigid.

Knowing AI Is Becoming A Basic Engineering Skill

This does not mean every mechanical, civil, electrical or electronics engineer needs to become a machine-learning specialist. Instead, AI literacy is increasingly becoming similar to digital literacy: something engineers across disciplines may need to understand well enough to use.

India’s IITs are already responding to this shift. A recent review found more than 50 AI-linked programmes across the country’s 23 IITs, spanning degrees, diplomas and shorter courses. AI is also moving beyond computer science into areas such as manufacturing, healthcare and robotics.

At IIT Mandi, for example, data and AI coursework is being incorporated into undergraduate engineering education. IIT Ropar has a dedicated B. Tech in Artificial Intelligence and Data Engineering, while IIT Delhi offers postgraduate programmes covering machine intelligence and data science. IIT Madras and IIT Bombay have also expanded options for students and working professionals to acquire AI skills outside a conventional undergraduate route.

“Modern engineering roles no longer stay within one discipline. A mechanical engineer today works with simulation software, sensor data, cloud dashboards alongside traditional blueprints. Software engineers work with automated pipelines and AI tools as part of product development. While four-year degrees build strong analytical foundations, workplaces increasingly demand cross-disciplinary agility, real-time decision-making, and the ability to adapt to rapidly changing tools. That pace of change is difficult for static academic curricula to fully capture,” said Mavani.

What Engineers Should Know About AI Disruption?

Perhaps the most important skill in the AI era is not generating an answer. It is judging whether the answer deserves to be trusted.

AI-generated code can contain bugs. AI-generated analysis can make incorrect assumptions. A design can look convincing on a screen but fail under real-world conditions. In engineering, where safety, cost, regulations and reliability matter, the ability to validate an AI output becomes critical. This is why the fundamentals of engineering may actually become more valuable, not less.

“Core engineering fundamentals still matter. You can’t properly evaluate an AI-generated design without understanding the principles behind it. But engineers also need data literacy, an understanding of how to work effectively with AI tools, and basic system architecture to evaluate outputs and identify errors. Most importantly, engineers retain accountability. AI can suggest, but the human engineer signs off. As tools automate more of the execution, responsibility for safety, accuracy, and system logic becomes even more important,” Mavani explains.

An engineer who understands materials, structures, electrical systems, thermodynamics, software architecture or manufacturing processes has the knowledge needed to challenge an AI-generated solution. Without that foundation, an engineer risks becoming an efficient user of tools without knowing when those tools are producing something unsafe or simply wrong.

The engineer of 2026 therefore needs both technical depth and the ability to work with AI.

What Should Engineering Syllabus Contain?

“India is still mostly ‘updating’ rather than fully redesigning (the syllabus). Many institutions are adding AI, data science and emerging technology modules and courses. At the same time, students are already using AI tools in their day-to-day work, often outside the formal curriculum. That creates a gap: education may teach the topic, but the workplace expects applied competence, judgment, and responsible use. This is where a true redesign is needed, with stronger focus on problem framing, logical reasoning, communication, and language proficiency, alongside verification, ethics, and real-world delivery. These skills were always important, but AI has made them decisive,” said Medury.

For example, BITS Pilani’s Work Integrated Learning Programmes (WILP) leads this structural shift, offering AI-centric, industry-aligned learning across mechanical, electrical, and software engineering. Other universities are also introducing dedicated AI, data science, and interdisciplinary technology programmes, signalling a broader shift towards industry-aligned learning.

“But the real roadblock is reach; many colleges still lack trained faculty and lab infrastructure to follow,” Mavani points out.

What Should An Engineer Learn Now?

For a fresh graduate, the priority should be a strong combination of fundamentals, practical projects and AI fluency. Knowing how to use AI coding or design tools is useful, but building something with them, and being able to explain why it works, where it fails and how it was tested, is far more valuable.

“Engineering fundamentals will always matter. But today’s engineers also need a design-thinking mindset — to be able to ask the right questions, understand the people and business behind a problem, and think beyond the immediate technical solution. Even writing an effective AI prompt requires clarity on the problem you are trying to solve and the outcome you want to achieve,” said Ritwik Batabyal, CTO and Innovation Officer at Mastek.

For an engineer with a few years of experience, the emphasis can shift towards automation, system integration, data and domain-specific AI applications. This is also where communication becomes increasingly important because many emerging roles require engineers to work directly with customers, product teams and business leaders.

“Working engineers, across software and physical disciplines, need to become comfortable with AI-assisted workflows, data, and modern digital systems. However, the ultimate meta-skill is adaptability. Technological cycles move too quickly for rigid specialisation. The ability to unlearn outdated approaches, communicate clearly with AI tools, and combine domain expertise with digital capabilities is what keeps an engineering career resilient. Learning, unlearning, and relearning are no longer optional; they are essential to staying relevant,” said Mavani.