AI Jobs Are Growing. But What Skills Are Companies Actually Looking For?

If you are a student preparing for a career in technology, you have probably heard two completely different versions of what is happening in the job market. One says artificial intelligence is reducing jobs and making entry-level opportunities harder to find. The other says AI is creating thousands of new jobs and could become one of the biggest career opportunities of the next decade. When both narratives appear almost every day, it becomes difficult for students to understand what they should actually do.

The more useful question isn't whether AI will create or replace jobs. For someone preparing to enter the industry, the question is much simpler: what should I actually learn to become employable in a job market increasingly influenced by AI?

Fresh hiring data from India gives us an interesting clue. According to Naukri JobSpeak data for September 2026, overall white-collar hiring in India grew only around 2% year over year, while hiring for AI and machine-learning roles increased approximately 20%. This doesn't mean every student should immediately abandon software development and become a machine-learning engineer. Instead, it suggests that technology hiring is becoming increasingly specialised and that AI capabilities are gaining importance within the broader technology job market.

Companies still need software developers, cloud engineers, cybersecurity professionals, data engineers and other technology specialists. What is changing is that AI is increasingly becoming part of these roles rather than remaining an entirely separate career category. A software engineer may now work with AI-assisted development tools. A data engineer may build pipelines for AI applications. A cybersecurity professional may need to secure AI agents. A cloud engineer may be responsible for infrastructure that runs AI workloads.

This changes how students should think about learning AI. Instead of asking, “Should I learn software development or AI?”, a better question may be, “What strong technical skill can I combine with AI?”

Someone who knows Python and understands how to call an AI model can certainly build interesting applications. But someone who understands Python, APIs, databases, Git, software architecture and AI can potentially build applications that are reliable enough to solve real problems. That distinction becomes important when students move from following tutorials to competing for internships and jobs.

One of the best ways to understand this shift is to look at what employers actually ask for instead of relying entirely on course advertisements or social-media advice. Current entry-level AI and machine-learning opportunities in India increasingly mention practical projects alongside technical knowledge. Employers are looking for candidates who can demonstrate experience with areas such as Python, APIs, large language models, retrieval-augmented generation, embeddings, databases, Git and common AI frameworks. Depending on the role, exposure to Docker, cloud platforms, backend frameworks and deployment can also become valuable.

There is an important pattern behind these requirements. Companies are not simply asking candidates whether they “know AI.” They increasingly want to understand whether candidates can build with AI.

That is why strong fundamentals still matter. Before trying to learn every new AI framework, students should become comfortable writing and understanding code. Knowing Python syntax is useful, but knowing how to solve a problem, structure a program, debug an error and understand why a piece of code behaves the way it does is far more valuable.

Software-engineering knowledge becomes equally important. Understanding how APIs work, how applications interact with databases, how authentication works, how Git and GitHub are used, how backend systems are structured and how applications are deployed may sound less exciting than building the latest AI agent, but these skills often determine whether an AI prototype can become a usable application.

Once those foundations are developing, students can build deeper AI capabilities. They can explore machine-learning fundamentals, large language models, embeddings, retrieval-augmented generation, vector databases, structured outputs, tool calling and agentic systems. The important thing is not to confuse knowing the name of a framework with understanding the underlying concept. Frameworks can change quickly. The ability to understand and solve engineering problems lasts much longer.

Projects then become one of the most important parts of the learning process. But simply building another project from a YouTube tutorial may not provide much differentiation. A better approach is to start with a problem and then determine how technology can solve it.

Instead of creating another generic “PDF chatbot using RAG,” for example, a student could ask where document retrieval could genuinely solve a problem. They might build a system that helps students navigate complicated university regulations, an assistant that allows a small business to search hundreds of internal documents, or a tool that analyses technical documentation and surfaces relevant troubleshooting information.

The underlying technology may still involve retrieval-augmented generation, but the project now demonstrates something more valuable than knowledge of RAG. It demonstrates the ability to identify a problem, design a solution and apply AI appropriately.

Building the project, however, is only part of the process. Students also need to make their abilities visible. A useful project should not disappear inside a laptop after it is completed. The code can be maintained on GitHub, accompanied by a clear README explaining the problem, architecture, technologies used and important engineering decisions. Where practical, the application can be deployed and included in a portfolio alongside an explanation of what was learned while building it.

Most importantly, students should be capable of explaining their own work without depending on AI. If someone asks why a particular database was selected, how retrieval works, what happens when an API fails, why a certain architecture was chosen or what limitations the application has, the person who built the project should be able to discuss those decisions confidently.

This becomes particularly important because AI tools have dramatically reduced the effort required to produce software. A student can now create an impressive-looking application much faster than before. But generating something that works once is different from understanding how it works, testing it, debugging it and improving it.

Another challenge students face is the temptation to collect AI tools. Almost every week brings a new model, framework, coding assistant or agent platform. Trying to learn all of them is both exhausting and unnecessary. Instead of becoming a collector of tools, students can focus on developing transferable capabilities. Being able to build an API, work with a database, implement retrieval, evaluate an AI response, debug unreliable behaviour, deploy an application and explain technical decisions is more durable than knowing every new framework that appears.

This is also why combining AI with another discipline can be more powerful than learning AI in isolation. A computer-science student can combine software engineering with AI. Someone interested in data can combine data engineering with AI. A cybersecurity student can learn how AI systems and autonomous agents need to be secured. An electronics student can explore embedded AI and edge computing. Someone interested in cloud infrastructure can learn how AI applications are deployed and operated at scale.

The goal does not necessarily have to be becoming “an AI engineer.” The bigger opportunity may be becoming someone who understands a valuable field deeply enough to use AI within it.

The recent hiring numbers should therefore be interpreted carefully. A 20% year-over-year increase in AI and machine-learning hiring does not mean learning AI automatically guarantees employment. Hiring statistics describe what is happening across a market; they cannot predict an individual student's career outcome. But they do provide a useful signal that AI capabilities are becoming increasingly relevant while employers continue to look for candidates who can demonstrate practical skills and job readiness.

For students, that creates a clearer strategy. Build strong technical fundamentals first. Develop an AI specialisation on top of those fundamentals. Apply what you learn to real problems. Build projects that demonstrate your capabilities. Document those projects properly. Make your work visible through GitHub and a portfolio. And learn how to explain your technical decisions confidently.

Because as AI tools become accessible to almost everyone, simply being able to say “I learned AI” becomes less meaningful.

The question that increasingly matters is much more practical:

What can you actually do with it?

And ideally, your projects, GitHub, portfolio and ability to explain your work should answer that question before you even have to.