Reading code, fundamentals, testing, problem definition, AI integration, data responsibility and communication.
With AI-assisted coding tools now widespread, people often ask: “Do we still need junior developers?” Our conversations with employer partners give a clear answer: the need remains, but the expected skill set is changing. Here are the skills that stand out in 2026.
1. Reading code matters more than writing it
AI tools can produce code quickly, but deciding whether that code is correct, secure and right for the project is still a human job. Understanding code written by others, debugging and reviewing code have become basic expectations even at junior level.
2. Fundamentals are still fundamental
Without fundamentals such as data structures, HTTP, database relationships and Git, it is impossible to judge AI-generated code. That is why we keep AI coding assistants switched off for the first four weeks of our programmes.
3. Testing and verification
The biggest risk with generated code is a solution that “seems to work” but fails in edge cases. Developers who can write tests and think about what a change might break are becoming more valuable.
4. Defining the problem well
Good AI output starts with a well-defined problem. Clarifying requirements, talking to the product team and breaking work into small pieces have become an inseparable part of technical skill.
5. Building AI into the product
Junior job ads increasingly ask for the ability to integrate large language model APIs into an application, design prompts and build simple RAG solutions that work with company data. We cover these skills in the final part of our Data Science programme and in an elective module of the Full Stack programme.
6. Security and data responsibility
Sending personal data to an AI service can have serious consequences under KVKK. Knowing which data goes where and keeping secrets out of code is now a responsibility expected of junior developers too.
7. Communication
In remote and hybrid teams, written communication, a good pull request description and a clear bug report matter as much as technical knowledge.
How to build these skills
Making small contributions to open source projects is the best practice for reading other people’s code. Defining each feature with a test first in your own projects and reading AI suggestions line by line before accepting them are also good habits. On our programmes every participant reviews at least two classmates’ code each week.
Conclusion
AI is not eliminating the junior developer’s job; it is changing it. As routine coding speeds up, thinking, verification and communication skills come to the fore. We update our curriculum every cohort to reflect this change.