Which Computer Science specialisation suits a BTech graduate with a 7.75/10 CGPA and four years of experience in software development?

At four years in, your work experience matters more to admissions and to your career than a 7.75 CGPA does on its own. The stronger question is which specialisation extends what you have already built rather than which one sounds the most in-demand.

Distributed Systems and Software Engineering: The Natural Progression

• Best if your four years are backend or full-stack: This path deepens distributed computing, database systems, cloud architecture and system design, feeding directly into Senior Software Engineer, Tech Lead or Software Architect roles.
• Why it edges out AI/ML as a default: It extends your existing strength instead of asking you to prove a new one from scratch.

AI and Machine Learning: The Right Pivot, With a Caveat

A genuine interest in mathematics, statistics and experimentation is what should justify this choice, not just its popularity, since the field rewards strong foundations in probability and linear algebra rather than a software background alone.

Data Engineering: The Smoother Bridge From Software Development

If your interest is data without a full pivot away from engineering, Data Engineering is generally an easier transition from a software development background than pure Data Science, since your existing skills in pipelines, cloud platforms and distributed systems transfer directly.

My Advice

In my experience, developers at the four-year mark often either stay too generic or chase AI/ML purely because it is trendy, and both choices dilute a strong software background. Match the specialisation to what you have actually been doing, backend work to Distributed Systems, data-adjacent work to Data Engineering, and only choose AI/ML if you would genuinely enjoy the mathematics, not just the job title.