How to get a software job as a fresher in the AI era

Getting a first software job has become harder to reason about. The advice that worked for your seniors, practise aptitude and solve enough coding problems, was built for a market where companies hired freshers in bulk and trained them afterwards. That market is changing.

This guide explains what is different and what to do about it.

What has changed

The junior tasks are being automated. The work freshers were traditionally given in their first year, small fixes, routine screens, test cases, is the work AI tools now do well. A company no longer needs a large bench of juniors to get it done.

Teams are getting smaller. One engineer with good tools covers more ground. So companies hire fewer people and need each one to be useful sooner.

A degree proves less than it did. When a tool can pass the same exam, employers put less weight on marks and more on evidence that you can do the job.

None of this means there are no jobs for freshers. It means the bar for a fresher has moved closer to what used to be expected after a year or two of experience.

What companies look for now

From what we see in hiring conversations, four things come up again and again:

1. Can you design before you build? Being able to sketch a data model and an architecture, and explain your choices.
2. Do you understand the problem, or only the code? Interest in what the business does and who uses the software.
3. Can you tell when something is wrong? Especially when an AI tool produced it and it looks fine.
4. Can you be left alone with a task? Evidence that you finish things and communicate along the way.

Coding ability is still required. It has become the entry ticket, where it used to be the whole test.

How to show it

Claims on a CV are cheap. What works is evidence.

• One or two real projects, not ten tutorials. A project with users, a deployment and a history of changes says more than a long list of clones.
• Design documents. Keep the data model, architecture and flow diagrams you made, and the reasons behind them. Bring them to interviews.
• A record of being reviewed. If someone has questioned your work and you improved it, write down what changed and why.
• Team work you can describe precisely. Know what you personally owned, what went wrong and what you did about it.
• Honest use of AI. Be ready to explain how you used AI tools, what you rejected from their output, and how you checked the rest.

What to stop doing

• Collecting certificates. Interviewers have learned to ignore them.
• Memorising interview answers. A follow-up question exposes it immediately.
• Hiding that you used AI. Everyone does. What matters is whether you understood the result.
• Waiting for placement season to start building. Evidence takes months to create.

A realistic plan

If you have a year or more before you graduate, spend it building one serious system with a team and getting it reviewed by people who do this for a living. If you have less time, take your best existing project, write the design document it should have had, and fix what that exposes.

The Techvisk programme gives you that structure: team builds, practitioner reviews and a record of both that companies can read. Our guide to AI-era skills covers what to learn in more detail.