India's Tech Hiring Is Rising. So Why Are Freshers Still Struggling to Get Hired?

If you are a student preparing for a career in technology, the job market can feel confusing right now. On one side, there are constant conversations about artificial intelligence changing jobs, companies restructuring teams and entry-level opportunities becoming harder to find. On the other, recent hiring data suggests that India's technology job market is actually showing signs of recovery. According to recent workforce-market data from Xpheno, active technology openings in India have reached approximately 123,000 in October 2026, around 17% higher than a year ago and the highest level in roughly 18 months. At first glance, that sounds like exactly the kind of news students and fresh graduates have been waiting for. More jobs should mean more opportunities. But when we look deeper into the numbers, the situation becomes more complicated.

A significant portion of these opportunities isn't necessarily meant for people entering the industry for the first time. Around 55% of active technology openings are reportedly concentrated in mid-to-senior positions, with demand particularly visible across AI engineering, analytics and technical leadership roles. This helps explain something many students may already be experiencing. The technology job market can improve while the fresher job market still feels difficult. These two realities do not contradict each other. When we hear that technology hiring is increasing, it is easy to imagine companies hiring equally across all levels of experience. In reality, organisations hire people to solve specific problems, and many of those problems currently require professionals who can become productive relatively quickly.

Consider a company introducing AI into its products, moving infrastructure to the cloud, developing enterprise software or automating an important internal process. The company may need someone who understands not only programming but also databases, APIs, deployment, security and the business environment in which that technology will operate. Hiring someone with several years of relevant experience can therefore feel less risky than hiring a graduate who still requires substantial training. This creates one of the oldest and most frustrating problems for students: employers ask for experience, but students need their first opportunity before they can gain that experience.

The solution is not to somehow manufacture three years of professional employment before graduation. Instead, students need to start thinking differently about what experience actually means. Professional employment is one form of experience, but it isn't the only way to demonstrate capability. A student who has spent a year building substantial projects, contributing to open-source software, completing internships, participating seriously in hackathons or solving real problems can demonstrate far more readiness than someone who has only completed coursework. The objective is to give an employer evidence that you have already started working like someone who belongs in the industry.

This is where projects become particularly important, but projects are also one of the most misunderstood parts of career preparation. Many students build projects because they have been told that projects look good on a resume. They follow a tutorial, reproduce the same recommendation system, sentiment-analysis application or basic chatbot that thousands of other students have already built, upload the code to GitHub and consider the project complete. There is nothing wrong with using tutorials to learn, but a tutorial project provides limited evidence of independent problem-solving because many of the important decisions have already been made for you.

A stronger project begins with a problem rather than a technology. A student might build an AI system that analyses customer-support conversations and identifies recurring issues, create a document-processing workflow that extracts and validates information, develop an application that helps students compare skills required across job descriptions or build an AI agent capable of interacting with several APIs while controlling which actions it is allowed to perform. The project itself does not need to become a startup or attract thousands of users. What matters is that the student has to make decisions while building it.

Those decisions are where engineering ability starts becoming visible. Why did you choose that architecture? Why did you use that database? What happens when an external API fails? How did you test the AI-generated responses? How are users authenticated? What happens if the model produces incorrect information? How does the application behave when it receives unexpected input? How did you deploy it? What would you change if the number of users increased dramatically? A student who can confidently discuss these questions demonstrates something far more valuable than simply knowing the names of several technologies. They demonstrate that they can think through a technical problem.

This becomes even more important as AI changes how software is created. Students now have access to tools capable of generating code, explaining errors, writing tests, creating documentation and even building significant parts of applications. These tools can dramatically accelerate learning and development, and students should absolutely learn how to use them. But they also create a new challenge. If almost everyone can generate code faster, simply possessing code becomes less impressive. The differentiator gradually moves toward whether you understand what was built, whether you can recognise when something is wrong and whether you can improve the system when the first solution doesn't work.

A recruiter or engineer looking at your project probably won't care whether you manually typed every line of code. They are much more likely to care whether you can explain the architecture, debug a problem, modify the application, justify your technical decisions and identify weaknesses in an AI-generated solution. Students therefore shouldn't avoid AI coding tools because they fear becoming dependent on them. They should learn to use those tools while maintaining enough technical understanding to remain responsible for the final result. AI can help you write the code, but you still need to understand what that code is doing.

GitHub can become valuable for exactly the same reason. Many students create a GitHub account because someone told them recruiters expect to see one. They upload a few repositories and then add the profile link to their resume. But GitHub can tell a much richer story about how someone works. A well-maintained repository can show how a project evolved, how features were added, how bugs were fixed and how clearly the developer communicates technical information. A thoughtful README can explain the problem being solved, the architecture, the technologies used, setup instructions, limitations and future improvements. The objective is not simply to have a GitHub profile. The objective is to create evidence that you actually build things.

A portfolio serves a similar purpose. Your resume may say that you know Python, JavaScript, React, FastAPI, SQL, machine learning or generative AI, but thousands of other resumes may contain exactly the same keywords. A portfolio gives you an opportunity to answer the question those keywords cannot answer: what have you actually done with those skills? Instead of simply showing a screenshot and project title, students should explain what problem they were trying to solve, how they approached it, what difficulties they encountered, what decisions they made and what they learned from the process. This transforms the portfolio from a collection of projects into evidence of problem-solving ability.

There is another reason this matters now. Students entering AI and technology careers aren't competing only with other fresh graduates. Experienced professionals are learning AI too. Recent learning-platform data indicates strong growth in AI enrolments among professionals, including people with many years of industry experience. Interest is also moving beyond introductory generative AI toward agentic AI, automation, cybersecurity, autonomous systems and practical AI implementation. That means a student may eventually compete for an opportunity against someone who understands AI and already has years of experience in banking, healthcare, manufacturing, software, consulting or another industry.

A student cannot compete with ten years of professional experience by pretending to have the same depth. What they can do is start developing depth much earlier than most students do. Instead of trying to learn every new AI framework that becomes popular on social media, choose a direction and build around it. If you want to become an AI engineer, learn programming properly, understand APIs and databases, learn how models are integrated into applications and develop familiarity with deployment, testing and evaluation. If you want to become a software engineer working with AI, strengthen data structures, algorithms, backend or frontend development, databases, Git and software architecture while learning how AI tools fit into modern development workflows. If you're interested in data and machine learning, build strong foundations in Python, SQL, statistics, data processing and machine learning before rushing toward every new AI framework.

The exact roadmap will differ depending on the career you want, but the underlying principle is simple: learn fewer things deeply enough that you can actually build something meaningful with them. Collecting technologies is not the same as developing capability. Knowing the names of twenty AI tools will rarely matter as much as being able to use a smaller set of tools to solve a real problem.

Internships can strengthen this evidence further. Students often focus almost entirely on getting internships at large technology companies because they assume the company name is what makes an internship valuable. But early-stage startups, smaller technology companies, research groups and open-source communities can sometimes provide much more practical exposure. The organisation may be smaller, but the responsibility can be larger. Working on an API that real users depend on, fixing production bugs, analysing actual customer data or contributing to an AI feature can teach far more than completing another isolated online course. The goal should not simply be to collect an internship certificate. It should be to experience what happens when your work has consequences outside a classroom.

None of these activities guarantees employment. There is no project, certification, portfolio or internship that automatically produces a job offer. But together they address the biggest disadvantage every fresher faces: an employer has very little professional history with which to evaluate you. Your projects reduce that uncertainty. Your GitHub profile reduces it. Your internships reduce it. Your portfolio reduces it. Your ability to explain your decisions during an interview reduces it. Strong technical fundamentals reduce it even further. Every piece of evidence gives an employer another reason to believe that you can contribute.

This is why India's improving technology hiring numbers should be interpreted carefully. Approximately 123,000 active technology openings is an encouraging signal. It suggests that companies are hiring and that technology careers continue to offer significant opportunities. But students shouldn't look at that number and assume the opportunity will automatically reach them. If a large portion of demand is concentrated among experienced professionals, the lesson for freshers isn't simply to submit more applications. It is to become more prepared before submitting them.

Instead of asking, "How many jobs should I apply for?" students may need to start asking a more difficult question: "If someone opens my resume today, what evidence shows that I can actually contribute?" Maybe the answer is a deployed project. Maybe it is an internship. Maybe it is an open-source contribution. Maybe it is a strong GitHub repository. Maybe it is a portfolio explaining how you solved several difficult problems. Ideally, over time, it becomes a combination of these things.

Getting the first technology job has always been difficult because employers have very little professional history on which to evaluate a fresher. AI isn't necessarily eliminating that challenge. In some ways, it may make proof of capability even more important because generating resumes, writing code, earning online certificates and creating basic projects is becoming easier. When almost everyone can produce the same signals, employers have to look deeper for evidence of genuine capability.

So if you are preparing for your first technology role, don't look at improving hiring numbers and assume everything has suddenly become easy. At the same time, don't look at a difficult fresher market and conclude that there are no opportunities. The opportunities exist, but the way students prepare for them needs to evolve.

The goal should not simply be to become qualified on paper. It should be to become someone who can demonstrate readiness before being given the job title. Build things. Break them. Fix them. Document what you learned. Put your work where people can see it. Understand the fundamentals behind the tools you use and gradually develop evidence that you can solve problems independently.

Because in a job market where employers increasingly want people who can contribute quickly, the strongest advantage a fresher can create is simple: don't wait for your first job to start building experience. Start creating evidence of what you can do before someone gives you the title.