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Hi, I'm Aman Kumar 👋

Full-Stack Developer & ML Engineer · B.Tech CS @ PTU '27 · Open to Internships 2026


About me

  • 🎓 Third-year CS student at Punjab Technical University, graduating May 2027
  • 💻 I build full-stack systems (MERN) and train ML models that solve real problems
  • 🔧 Cut API response time by 15% at my internship via MongoDB query optimization
  • 🌐 Built a real-time chat app, WebRTC P2P video streamer & distributed rate limiter from scratch
  • 🤖 Trained a NanoGPT-style language model, fraud detection classifier & fake news detection pipeline
  • 📬 Looking for SDE / ML internships — Summer/Fall 2026

🛠 Tech stack

Frontend React Next.js TypeScript JavaScript HTML5

Backend Node.js Express Socket.io WebRTC

Database MongoDB SQL

ML / AI Python PyTorch scikit-learn

Tools Git Docker Java


🚀 Project highlights

🔁 Distributed rate limiter

Token Bucket + Sliding Window algorithms · Node.js · Redis

Built a production-grade rate limiting system from scratch supporting both Token Bucket and Sliding Window strategies. Handles concurrent request bursts with configurable thresholds per route.


💬 Real-time chat platform

Socket.io · Node.js · MongoDB · React

Full-stack real-time messaging app with room-based chat, typing indicators, and persistent message history. Optimized for concurrent users with low-latency event delivery.


📹 P2P video streaming

WebRTC · Node.js · Signaling server

Peer-to-peer video calling platform with optimized signaling logic for unstable network conditions. Implements ICE candidate handling and STUN/TURN fallback.


🗄️ Column-store database engine

Python · Dictionary encoding · RLE compression

Built a columnar storage engine from scratch with dictionary encoding and Run-Length Encoding (RLE) compression — mimicking core concepts behind analytical databases like ClickHouse.


🤖 NanoGPT-style language model

PyTorch · Transformer architecture · Character-level tokenization

Implemented a GPT-style autoregressive language model from scratch including multi-head self-attention, positional encoding, and training loop with learning rate scheduling.


🛡️ Credit card fraud detection

Python · scikit-learn · Class imbalance handling (SMOTE)

Trained a fraud classifier on imbalanced financial data using SMOTE oversampling and ensemble methods, achieving high recall on minority class (fraud cases).


📊 GitHub stats


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Open to SDE & ML internship opportunities · Summer/Fall 2026 · amankumar005562@gmail.com

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