AI Is Changing Campus Hiring. What Should Students Do Differently?

For years, the career path for many engineering and technology students in India followed a fairly predictable pattern. Complete your degree, prepare for aptitude tests, practise common interview questions, sit for campus placements and hope that one of the large technology companies selects you.

That model isn't disappearing overnight. But it is changing.

AI is allowing technology companies to automate more work, improve productivity and rethink how many people they need for certain tasks. At the same time, companies are looking for people who can work with AI, build with it and apply it to real business problems. This creates a situation that can feel contradictory for students: AI opportunities are growing, but getting the first technology job can still feel difficult.

Recent comments from Nasscom President Rajesh Nambiar help explain why. India's technology industry historically followed a relatively linear growth model. When companies won more business, they generally needed more employees to deliver that work. AI has started changing that relationship because companies can potentially generate more output without increasing headcount at the same rate.

This matters particularly for fresh graduates. Large IT and BPM companies have historically absorbed significant numbers of students through campus recruitment. If those companies can now improve productivity using AI, automation and more efficient delivery models, hiring does not necessarily have to increase at the same speed as business growth.

That does not mean there will be no jobs for freshers. It means students may need to prepare for a different kind of job market.

The old question was often, “How do I get placed?”

The better question now may be, “How do I become valuable enough that a company wants to hire me?”

That sounds like a small difference, but it changes the entire approach to career preparation.

A degree still matters. Programming fundamentals still matter. Aptitude, communication and interview preparation still matter. But when thousands of students graduate with similar degrees and increasingly have access to the same AI tools, simply completing the expected curriculum becomes a weaker way to differentiate yourself.

Students need evidence of what they can actually do.

Imagine two computer science graduates applying for the same entry-level technology role. Both have similar degrees. Both know Python. Both mention artificial intelligence on their resumes. Both have completed online courses.

The first candidate lists “Machine Learning, Python, Generative AI and RAG” under skills.

The second candidate has built an AI application around a real problem, maintained the project on GitHub, documented the architecture, deployed a working version, evaluated its performance and can clearly explain what went wrong while building it.

Their resumes may contain many of the same keywords.

Their evidence is completely different.

That difference is becoming increasingly important.

AI has also created another interesting problem. Building something that looks impressive has become much easier. Students can use coding assistants and AI agents to generate interfaces, APIs, database schemas and even complete applications. A project that once took weeks may now be prototyped in days or sometimes hours.

But easier building does not automatically mean stronger engineering.

If an interviewer asks why you selected a particular database, how authentication works, what happens when an API fails, why your retrieval system returns incorrect information, how you evaluated the model or how you would handle thousands of users, you still need to understand the system.

AI can help you build the project.

It cannot replace your understanding of the project when someone starts asking questions.

That is why students should be careful about interpreting “learn AI” as “learn more AI tools.”

The number of tools you know is becoming less meaningful because the tools themselves change constantly. A new model, framework, coding assistant or agent platform appears almost every week. Trying to master all of them is neither realistic nor necessary.

Instead, students should develop capabilities that remain valuable even when the tools change.

Learn how programming actually works. Understand APIs. Work with databases. Learn Git and GitHub. Understand basic software architecture. Learn how applications are deployed. Become comfortable debugging. Then understand how AI models, embeddings, retrieval, agents and evaluation fit into that engineering foundation.

The combination matters more than either side alone.

A student who understands software engineering and AI can potentially become far more useful than someone who has only experimented with dozens of AI tools.

The same principle applies outside software development.

Someone interested in cybersecurity can combine cybersecurity with AI. An electronics student can explore embedded systems and AI. Someone interested in cloud infrastructure can learn how AI workloads are deployed and operated. A data student can combine data engineering with AI. Even areas such as semiconductor engineering, healthcare, finance, manufacturing and biotechnology are increasingly integrating AI.

This is why students should stop thinking of “AI” as one career.

AI is increasingly becoming a layer across many careers.

The next generation of technology professionals may not all have job titles containing the words “Artificial Intelligence.” They may be software engineers who work with coding agents, cybersecurity professionals who secure autonomous systems, cloud engineers who manage AI infrastructure, data engineers who build pipelines for AI applications or domain specialists who use AI inside industries such as healthcare and finance.

For students, this makes career direction more important.

Instead of randomly collecting courses, choose a direction first. Look at actual job descriptions for the role you want. Identify the skills that repeatedly appear. Understand which ones you already have and which ones you are missing. Then build projects that force you to practise those skills.

Your learning roadmap should come from the career you want, not from whatever technology happens to be trending that week.

Projects should also become more intentional. Instead of asking, “Which AI project should I copy?” ask, “Which problem can I solve that demonstrates the skills required for the job I want?”

If you want to become an AI engineer, build something that demonstrates more than an API call. Work with retrieval, evaluation, databases, backend systems and deployment. If you want to become a software engineer, build an application where AI is one useful component rather than the entire project. If you want to enter cybersecurity, explore how AI systems introduce new security problems and build around those challenges.

Then document the work properly.

Your GitHub should show more than uploaded code. Your README should explain what you built, why you built it, how the system works, what technologies you used and what limitations remain. Your portfolio should help someone understand your abilities without needing a 30-minute conversation with you.

Most importantly, be able to explain everything yourself.

This is where career readiness is moving.

The goal is no longer simply to have a certificate proving that you completed something. The goal is to create enough evidence that another person can evaluate your ability.

That evidence could come from projects, internships, hackathons, open-source contributions, GitHub repositories, technical writing, deployed applications or meaningful work done for real users.

None of these individually guarantees a job. But together they can tell a much stronger story than a resume filled entirely with course names and skill keywords.

Students should also reconsider how much they depend on campus placements. Campus opportunities remain valuable and should absolutely be pursued where available. But they should be one part of a broader job-search strategy rather than the entire strategy.

Students can explore internships, startup roles, global capability centres, smaller technology companies, open-source communities, hackathons and direct applications. They can connect with professionals working in their target roles, study what those professionals actually do and gradually build evidence that aligns with those opportunities.

The shift happening in technology hiring is therefore not simply a story about AI replacing jobs.

It is a story about the value of different skills changing.

When companies can automate more routine work, the ability to understand problems, design systems, make technical decisions, validate AI-generated work and apply technology to real situations becomes increasingly valuable.

For students, the response should not be panic.

It should be preparation.

Don't stop learning programming because AI can write code. Learn programming deeply enough to understand whether the code AI produces is correct.

Don't build projects only to add another line to your resume. Build projects that demonstrate what you can actually do.

Don't learn AI because everyone else is learning AI. Understand where AI fits into the career you want.

And don't wait until the final semester to start thinking about employability.

The technology industry is changing quickly, and the path from college to career is changing with it. Students who understand that shift early have something valuable: time to prepare differently.

The question is no longer simply:

“Will my college placement get me a job?”

A more useful question is:

“If I had to prove today that I can create value for a technology company, what evidence would I show?”

If you don't have a strong answer yet, that is probably the best place to start.