How Indian Software Engineering Freshers Should Prepare for Jobs in the Agentic AI Era
India’s largest IT employer is doing two strange things at once. TCS plans to hire roughly forty-two thousand freshers in FY26. It is also cutting about 12,200 jobs, primarily affecting middle and senior management, as it deploys AI and other technologies. If that sounds contradictory, it isn’t. The pyramid is being reshaped while the music is still playing. Agentic AI is the chisel.
For software engineering freshers and final-year students in India, the takeaway sits somewhere between unsettling and freeing. The doors are still open. The doors look different. The people who get through them are not the ones with the cleanest CGPA or the longest list of certifications. They are the ones who understood, sometime in the third year, that the rules had quietly changed.
This guide covers what has actually changed in the Indian fresher market, what hiring managers at global capability centres, product startups, and even the big service companies now look for, and what you should be doing this semester if you want a real offer when placement season hits.
The Indian fresher market is splitting in two
There used to be one path. Engineering degree, campus placement, two months of training at the company’s facility in Mysuru or Hyderabad or Trivandrum, and a slow climb. That path still exists, but it now sits next to a second, much steeper one that pays differently, screens differently, and rewards completely different things.
On one side you have the mass-hire service companies, the TCS NQT and Infosys InfyTQ funnel. They are still hiring. They have to. But the work they put freshers on is shrinking because the AI tools are eating the easier end of it.
On the other side you have the GCCs that have set up shop in Bengaluru, Hyderabad, and Pune. Walmart, JPMorgan, Goldman, Wells Fargo, Target, and their cousins. The EY GCC Pulse Survey 2025 found that 58% of Indian GCCs are already investing in agentic AI, while 83% are scaling generative AI projects. These are the places where the interesting work and the interesting compensation are now concentrated. Then a third lane runs alongside, made up of Indian product companies and AI-first startups, where what you can build matters more than where you studied.
The fresher problem is that almost everyone is still optimising for door one while the action has shifted to doors two and three. This is the single biggest misalignment in the Indian engineering pipeline today.
What “AI native” actually means, and what it doesn’t
TCS told analysts last month that it was particularly keen on hiring “AI natives”, described as young people adept at using a wide range of modern AI tools in their jobs. Read that sentence twice. The signal is in the phrase wide range of modern AI tools in their jobs. Not “uses ChatGPT”. Not “took a Coursera course on prompt engineering”.
Being AI native in 2026 means a few specific things. You ship code with Claude Code, Cursor, or Copilot every day, and you have opinions about what each one is good and bad at. You know how to break a problem into pieces an agent can handle, run them in parallel, review the output, and reject what is wrong. You have built at least one project where an LLM is not the feature, but the engine. You know what an eval is and why your output is useless without one. You can name the difference between RAG, fine-tuning, and tool use, and you know when each one is the wrong answer.
If you want to specialise further, the agentic stack people get hired against right now includes LangGraph, CrewAI, AutoGen, vector databases, and production deployment. LinkedIn India data shows job postings requiring LangChain, CrewAI, or “AI agent” skills grew by over 300% between January 2025 and March 2026. The honest caveat is that because the field is so new, most companies are promoting GenAI engineers into agentic roles rather than hiring fresh. So your fresher route is not a course and a certificate. It is build, demo, deploy, repeat.
Build over grind, the portfolio shift Indian students keep getting wrong
I want to say this carefully because LeetCode rage-bait is its own genre now, and I do not want to add to it. Data structures and algorithms still matter. The TCS NQT, Infosys assessment, and most product company first-rounds are still gated on them. If you cannot pass the screen, the rest does not matter.
But solving five hundred LeetCode problems is not the strongest signal you can send anymore. It used to be. It isn’t now, because an LLM solves most of them in seconds, and hiring managers know it. The new strongest signal is a portfolio of two or three projects that actually run, that someone other than you has used, and that you can talk about for forty-five minutes without notes.
If you are stuck on what to build, here is a working set of ideas. A research agent that takes a topic and produces a Notion-ready brief with sources. A WhatsApp bot in Hindi or Marathi that helps small business owners track invoices, built using Bhashini APIs. A code review agent for your college project that catches the specific kind of bugs your batch keeps making. A multi-agent system that takes a startup idea, generates a competitive landscape, validates the market size, and drafts a pitch deck outline. The exact projects matter less than the loop they pull you through. You will hit production problems. You will discover what evals are for the painful way. You will end up reading documentation for tools nobody in your college has heard of. That is the whole point.
Open-source contributions are now overweighted by hiring managers who have learned not to trust polished resumes. Pick a popular AI library you actually use. Send three real pull requests over the next six months. Document the journey publicly on LinkedIn or X. You are now in the top one percent of Indian fresher candidates by demonstrated initiative, regardless of which college you attend.
Why CS fundamentals matter more, not less
There is a tempting bit of logic going around that says, if AI writes the code, the fundamentals do not matter. This is backwards in a way that takes a few years to notice.
When AI writes the code, the human’s job moves up the stack. You are no longer typing the function. You are deciding whether the function should exist. You are reading the agent’s output and catching the database call that will time out at scale. You are debugging the production incident at 2am when the AI agent has gone in a loop and burned through twelve thousand rupees of API credits. You are designing the system the agents operate inside.
All of that requires a deeper grasp of fundamentals, not a shallower one. Operating systems. Networks. Distributed systems. SQL and how a query planner actually decides things. The cost model of a cloud call. The security implications of giving an autonomous agent access to your production database. None of this is going away. The freshers who treat their CS coursework as box-ticking and the AI tools as a shortcut around understanding will find themselves boxed into the work that is most exposed to automation. The ones who go deeper on the fundamentals and use AI to multiply their throughput will find the opposite.
Read Designing Data-Intensive Applications. Take the MIT 6.824 distributed systems lectures on YouTube for free. Do one real database project where you understand every index. This is unglamorous advice, and it works.
Pick a domain before you pick a stack
The Indian engineering instinct is to stay a generalist as long as possible. Try everything, commit to nothing, see what the campus interviews offer. This was reasonable in 2018. It is now a slow career mistake.
Domain expertise compounds. Tech stacks change every two years. A fresher who has spent eighteen months getting deep into how banks process payments, or how clinical trial data is structured, or how agricultural supply chains break, is much more valuable to a GCC or a product company than one who knows six languages and no problem space. The phrase to remember is from a course advisor I respect: “An AI-in-domain programme is better. It applies agentic AI techniques inside a domain the candidate already knows, reducing ramp time and increasing hireability in GCCs and product companies.”
Indian domain bets that look strong over the next five years include financial services (Mumbai is still the BFSI capital, and the Indian fintech build-out is far from finished), healthtech (the public stack and private insurance are both digitising fast), agritech (huge underbuilt market, especially around supply chain and price discovery), public-sector tech (India Stack, ONDC, the Digital Public Infrastructure stack the world is now copying), and Indic-language AI (Sarvam, Krutrim, Bhashini, and a long tail of startups building for the next four hundred million Indians coming online). Pick one. Read its trade press. Build a project inside it. You will be a more interesting candidate to anyone hiring inside that space than the generalist with a slightly better CGPA.
The geography and channel reality
The agentic AI work in India is currently concentrated in three cities, with Mumbai as a fintech-flavoured fourth. Bengaluru is the centre of gravity. Hyderabad is the fastest-growing challenger and now hosts an enormous chunk of GCC capacity. Pune has a quieter but real cluster of product companies and engineering services with serious AI work. If you are open to relocating to any of these, you have many more options.
Remote roles for US and European companies have changed the math as well. A fresher in Coimbatore or Bhubaneswar with a strong portfolio can now reasonably target remote positions with global firms, which was not really true four years ago. This is not the default path. It requires more proactive outreach and a public body of work. It is, however, a real path now in a way that placement-cell-only candidates rarely consider.
On channels, campus placement is one route, not the only route. For the fast lane, cold outreach to fifty startups beats applying to two thousand jobs on Naukri. The conversion rates are not even close. Pick twenty Indian AI startups whose product you actually find interesting. Email the founder or the head of engineering directly, with a specific suggestion or a small thing you built relevant to their product. Two will reply. One will give you an internship interview. This is how the market actually works above the mass-hire layer, and it is hidden from most college students because nobody teaches them.
What AI cannot do, which is where your edge lives
Spend any time around senior engineers who use these tools well and a pattern emerges. The work that gets harder to automate, not easier, is the work humans were never trained to think of as engineering. Writing a clear PRD. Listening to a non-technical stakeholder describe a problem and translating it into a system. Sitting in a meeting and noticing the unspoken thing the team is avoiding. Code review judgment when two reasonable implementations exist and one is going to bite in eight months. The ability to debug a production incident where the logs are lying and the metrics are stale.
If I were a fresher today, I would spend deliberate time on the communication side. Write more. Publish more. Practice explaining hard technical things in plain language. Get into rooms with product managers and designers and absorb how they think. Volunteer to write the design doc nobody wants to write. The freshers who do this end up being unusually employable in five years for reasons that look like luck from the outside.
The risk if you sit this out
This is the part most career advice in India avoids saying out loud, and I think that is a disservice. The Indian government’s policy think tank, Niti Aayog, in a report in October said, “Supply for AI talent is now 50% of the current demand in India and is expected to further lag in the next few years.” The same report warns that in a business-as-usual scenario, the headcount in the tech services sector could go down from 7.5 to 8 million in 2023 to 6 million by 2031, but taking corrective action and pushing for AI upskilling could increase the number of jobs in the IT sector to 10 million.
Read that twice. The same five-year window contains both a four-million-job shrink and a two-million-job expansion. Which one happens to you depends almost entirely on what you do in the next twelve months. A fresher from a tier-three college with a CS degree, no real projects, and a vague familiarity with ChatGPT is walking into the toughest entry-level market in twenty years. The same fresher with three deployed projects, a domain specialisation, demonstrated open-source contributions, and fluency in modern AI tooling is walking into the strongest one in twenty years. The college matters less than it used to. What you have done matters more.
This is the actual situation. The market is not collapsing. It is sorting.
