Trained in computer
science.
I’m Karan V — an AI/ML engineer and full-stack developer. I studied computer science because I wanted to understand how software actually works underneath; I stayed in it because I like the moment a project stops being a local file and becomes a URL someone else can open.
From coursework to production.
Where the foundation got built
A Computer Science degree gave me the fundamentals that don’t go out of style — data structures, algorithms, databases, operating systems, and how networks actually move information. Everything I build still traces back to that base.
Learning by breaking things
My early projects were small, rough, and mine — a personal site, a scraper, a to-do app that definitely had bugs. I learned more from debugging those than from any lecture, and that habit of building to learn never left.
Going end to end
I stopped being satisfied with just the frontend or just the backend. I wanted to own the whole chain — interface, server logic, database design, and the server it all runs on. That’s the full-stack instinct that still drives how I scope every project.
Making machine learning useful
Machine learning stopped being interesting to me as theory the moment I saw it solve a real problem — sorting images, understanding text, flagging what a human would otherwise miss. Since then, I build AI as a feature inside real products, not as a standalone experiment.
Karan V — built and deployed
Now I take on projects end to end: websites, web apps, mobile apps, e-commerce platforms, and AI systems — designed, built, deployed, and kept running. Twelve of them are live right now.
Idea → Build → Deploy → Maintain
Four stages, no shortcuts. Skip one and the project either never ships or doesn’t survive contact with real users.
Ask before you build
What problem is this actually solving, for whom, and what does “done” look like? Getting this wrong makes everything after it wasted effort.
Write it to last
Clear architecture, sensible schemas, and code a future version of me can still read six months later.
Ship it for real
Not a localhost demo — a live server, a real domain, HTTPS, and a deployment process that doesn’t depend on luck.
Stay with it
Monitoring, patching, and small improvements after launch — because software that’s left alone quietly decays.
Skills, grouped by what they’re for.
Languages
Frontend
Backend & Data
AI / ML
Mobile
Deployment & Infra
Pick the version that fits the role.
Same person, three angles — grab whichever one matches what you're hiring for.
Live
Frontend Developer
HTML, CSS, React, Next.js, and interface-focused work — for frontend and UI-heavy roles.
Software Engineer
Full-stack, backend, and systems work — for general software engineering roles.
AI/ML Full-Stack Developer
Machine learning shipped as product features, end to end — for AI/ML-focused roles.