If you graduated recently and the campus placement season did not go the way you expected, you are not imagining the difficulty, and you are not alone. Many services companies have slowed or paused their large fresher intake, and that change has arrived suddenly enough to leave a lot of capable graduates confused about what to do next.
This series is for graduates heading into technology careers, meaning software, hardware, and computing roles, whatever engineering branch you studied. It does not cover the core non-computing streams such as mechanical, civil, or chemical engineering, which sit outside the area we work in and can advise on honestly.
This first article explains, calmly and with data, what has actually changed in entry-level engineering jobs in the technology sector, and then sets out the domains worth investing your time in. The aim is understanding and orientation, not alarm. The market has shifted, but technology careers have not ended.
What changed in entry-level engineering jobs
The first thing to understand is that the overall market is not empty. According to the staffing firm Xpheno’s April 2026 outlook, active tech job openings in India rose to around 119,000 in March 2026, a roughly nine percent increase. So companies are hiring.
The problem is more specific. The openings are concentrated at the experienced end, and entry-level roles have become harder to find. Reporting through early 2026 described entry-level hiring dipping while firms increasingly prioritise candidates who already have one or two years of experience.
Fresh graduates are the group this affects most directly, and the effect shows up in the data, not only in anecdotes. A widely cited 2025 study of payroll records by Stanford’s Digital Economy Lab found that employment for workers aged 22 to 25 in the occupations most exposed to AI fell by roughly 13 percent, with steeper declines for young software developers.
It is worth being precise about what “most exposed” means, because it is the opposite of what the phrase might suggest. The exposed occupations are white-collar and software roles, not manual or blue-collar work. The study uses United States data, so it is not a direct measurement of the Indian market, but the Indian press has reported it as an early warning for hiring here, and it matches the slower fresher intake that Indian firms are now describing.
A second shift sits alongside this one. Global capability centres, the in-house engineering centres that multinational companies run in India, are growing faster than the traditional services firms. They tend to hire for specific product and domain skills rather than a generic trainable batch.
Putting these together gives an accurate picture. Hiring exists and is even growing in total, but the old high-volume entry path, built on routine work that is now partly automated, has narrowed. The roles that remain reward a specific, demonstrable skill in a domain.
Why your choice of domain now matters more
For two decades the Indian IT services model could hire large batches of graduates, train them for a few months, and place them on routine work such as basic coding, testing, and maintenance. That routine work is exactly what current AI tools assist with most directly, which is why that single entry path has shrunk.
The roles that held up better are the ones tied to a particular technology domain, where the work depends on understanding a specific system rather than on doing routine tasks at volume. So instead of presenting yourself as a general fresher available for any work, it is now more effective to choose a domain and build real, visible skill in it. The rest of this article sets out the domains we think are worth that investment.
Where to focus: technology domains worth your time
This is a deliberately selective list. Because the series is about where to invest limited time, it includes only domains that depend on deep, system-specific skill and that look likely to stay valuable as the market changes.
Paths where AI tools already handle most of the routine entry-level work, and where junior hiring is shrinking fastest, are left out on purpose. Pointing a fresh graduate’s months of effort toward the most exposed work would not be responsible. No one can promise a fixed number of years, but these are the areas we would tell our own students to consider.
- Embedded systems, Linux and firmware. Software that runs on devices and close to hardware: drivers, board bring-up, real-time systems, and IoT. The talent pool has always been smaller than demand, and the work is hard to automate because it depends on specific hardware behaviour and real debugging. This is the domain TECH VEDA specialises in, through our Linux systems engineering training, though it is one option among several here.
- Semiconductor and VLSI design. Chip design, verification, and physical design. India is expanding its semiconductor work, and this domain suits electronics and electrical branches well. It is specialised and not easily automated.
- Cloud, DevOps and site reliability. Building and running the infrastructure that large systems depend on: deployment pipelines, observability, and reliability. Demand is steady because every product company has to operate its systems, and the skill is hands-on.
- Data engineering and applied AI/ML. Building the data pipelines and machine-learning systems that products rely on. The distinction that matters most: using AI tools as a user is now common and not by itself a job, while building and operating these systems is the deeper, durable skill.
- Cybersecurity. Protecting systems, testing them for weaknesses, and designing them to be secure. There is a persistent shortage of skilled people, and demand grows as regulation and attacks increase.
- Networking and telecom. Routing, 5G, and network software. It is specialised, steady, and frequently overlooked by graduates, which can make it less crowded.
How to choose a domain
Four questions help narrow the choice:
- What genuinely holds your interest? Depth takes months, and interest is what sustains it.
- What does your branch give you a head start in? For example, electronics branches into embedded and VLSI, while computer science branches into cloud, data, and security.
- How specialised and hard to automate is the work? This favours domains closer to hardware, infrastructure, and security.
- How much learning runway do you have? Some domains let you show useful skill within a few months, while others take longer to reach a hireable level.
Where this series goes next
Now that the situation is clear and the domains are set out, the articles that follow will be practical. They will cover the new expectation that a fresher be useful early rather than only trainable, how to build visible proof of skill instead of relying on marks alone, a closer look at what each domain demands and how to begin, how to use open-source contribution and internships to reach a first role, and a month-by-month plan for the graduate who has not yet been placed.
TECH VEDA’s own depth is in the embedded and systems domain, so some examples will come from there, but the approach applies to whichever of these domains you choose. If you take one thing from this article, let it be this: the market did not close, it changed shape, and the response is to choose a durable domain and build skill the new shape values.
Key takeaways
- This series is for graduates heading into technology roles across any branch; it does not cover core non-computing streams such as mechanical, civil, or chemical engineering.
- Total tech openings in India grew to about 119,000 in March 2026, so hiring is not absent; the shortage is specifically at the entry level.
- The roles under most pressure are white-collar and software roles where the routine work is the kind AI tools now assist with. This is not a manual or blue-collar effect, and hiring is shifting toward domain-specific roles, including at global capability centres.
- The series deliberately focuses on durable, specialised domains, embedded and systems, semiconductor and VLSI, cloud and DevOps, data and AI/ML, cybersecurity, and networking, and leaves out the most automation-exposed entry paths on purpose.
- The effective response is to choose one of these domains and build real, demonstrable skill in it rather than presenting yourself as a general fresher for any work.
Further reading
- Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI — Stanford Digital Economy Lab
- From bootcamp to bust: How AI is upending the software development industry — Reuters
- The end of ‘easy’ IT jobs? India’s tech workforce faces AI challenges — The Straits Times
- Tech jobs rise to 1,19,000 in March, but freshers still struggle — CIOL (Xpheno data)
- GCCs tighten grip on India’s tech talent market — The Economic Times
