Role explained
What the work involves
Software engineers still need to understand requirements, design systems, write and review code, test behaviour, work with data and support changes after release. AI tools may assist with tasks, but a tool’s output still needs validation against the real system.
Useful strengths include debugging, security awareness, system design, clear communication and the judgment to decide when automation helps. Show how you review and improve work, not only which tools you use.
OUTPUT 01
Software changes connected to a real user need
OUTPUT 02
Tests and reviews that catch important failures
OUTPUT 03
Clear reasoning about AI-assisted work and its limits
Skills and examples to demonstrate
- Fundamentals beyond prompt or tool familiarity
- Ability to verify generated code and protect sensitive data
- Ownership of quality, maintainability and delivery outcomes
Your Talent Passport
Make your work easy to understand
- Show the feature or system you worked on, not only the tools used.
- Explain how you tested, reviewed or corrected AI-assisted output.
- Describe your own decisions and the impact you can substantiate.
Make a plan
A practical 2027 learning checklist
Choose a few actions that fit your role and current level. A checklist is a starting point, not a credential requirement.
Work through these at your pace
- Strengthen one software specialty plus data and testing fundamentals.
- Practise debugging unfamiliar code and explaining the root cause.
- Use AI tools only within approved data and security boundaries.
- Keep a project note that explains decisions, verification and limitations.
Check the location and work mode
Where these roles are found
Software and AI work spans product, services and GCC teams in Bengaluru, Hyderabad, Pune, Chennai and Delhi NCR. Team expectations vary; review the role’s actual engineering responsibilities rather than assuming AI replaces a particular job family.