How I work
I like turning ideas into working prototypes quickly, then refining them with better structure, UX, and code quality. I’m comfortable moving between frontend UI, backend logic, algorithms, and creative tools.
Computer science student · Full-stack developer
Hi, I'm Prabhjot Singh. I build useful software and keep learning in public.
I focus on full-stack web apps, DSA practice, and product-style interfaces that feel thoughtful, fast, and practical.
About
Building a strong engineering foundation while shipping small, useful products.
What I’m looking for
I’m looking for internship opportunities where I can contribute to real products, learn from experienced engineers, and keep growing in software development.
Outside the screen
When I'm not coding, I'm usually doing one of these:
- Playing basketball — keeps me competitive and team-focused
- Reading about startups, product design, and tech
- Video editing and motion graphics for fun
- Exploring indie games and thinking about game design
Tech Stack
Frontend
HTML
CSS
JavaScript
TypeScript
React
Next.js
TailwindCSS
Bootstrap
Backend
Node.js
Express.js
Python
PostgreSQL
C
C++
FastAPI
Redis
scikit-learn
Docker
Tools
Git
GitHub
GitHub Actions
Jest
Projects
Selected work and experiments
FastAPI · React · PostgreSQL · pgvector
Featured
RAG-Powered Document Q&A
A full-stack Retrieval-Augmented Generation app: upload a PDF, ask questions in natural language, and get answers grounded strictly in the document — no hallucinated content. Built with pgvector for semantic search, JWT auth, per-user rate limiting, and multi-turn conversational memory. Deployed across Render, Vercel, and Supabase.
React · Node.js · PostgreSQL · Redis
Featured
Project-D
A full-stack multi-tenant SaaS platform for document processing and collaboration. Self-built JWT auth, role-based access control, BullMQ background processing, WebSocket real-time notifications, Redis caching, rate limiting, audit logging, and CI/CD pipeline.
Python · scikit-learn · FastAPI · Docker · GitHub Actions
MLOps Pipeline — Spam Classifier
An end-to-end MLOps pipeline around a TF-IDF + Logistic Regression spam classifier: a served FastAPI inference API, Docker containerization, and CI/CD via GitHub Actions. Includes model versioning with retrain/promote endpoints, A/B traffic routing between model versions, structured JSON logging, and a live monitoring dashboard.
Get In Touch
Currently open for new opportunities. Whether you have a question or just want to say hi, I'll try my best to get back to you!
Say Hello