🚀 Competitive Programmer | Full Stack Web Developer | AI Enthusiast
📍 B.Tech CSE | IIIT Vadodara
- Attained a maximum rating of 1792 (Expert) on Codeforces — Handle: HelloBuddyJr23.
- Secured a peak rating of 1962 (4 ⋆) on CodeChef — Handle: yohohohooooo.
- Earned a top rating of 2158 (Guardian) on LeetCode — Handle: HelloBuddyJr.
- Achieved Global Rank 8 in CodeChef Starters 177 and Global Rank 554 in Educational Codeforces Round 177.
- Delivered 15+ RESTful APIs for biometric attendance workflows, reducing data processing time by 40%.
- Programmed CRUD operations with filtering and pagination, supporting 500+ concurrent users with 95% system uptime.
- Applied input validation and centralized error handling, reducing production errors by 60% and improving consistency.
- Collaborated with frontend and product teams on API specifications, achieving 25% faster delivery and 90% fewer bugs
- Developed a website using Next.js and Tailwind CSS, improving mobile accessibility by 40%.
- Optimized component rendering and integrated lazy loading, reducing page load time.
- Enhanced API interactions and state management, improving load efficiency by 50%.
- Conducted code audits and implemented rigorous testing to strengthen security and maintainability.
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A full-stack web application for receipt storage and expense tracking. It leverages Tesseract.js OCR and a BERT-based NER model to automate receipt parsing with 95% accuracy, while interactive Recharts dashboards provide spending insights. Built with secure Clerk authentication, seamless upload workflows, and optimized APIs for smooth client–server communication. Tech Stack: React.js, Node.js, MongoDB, Tesseract.js, LayoutLM, Recharts, Clerk 🔗 Visit Repo |
An AI-powered predictive maintenance system for IoT sensor data. It generates synthetic datasets with 10,000+ entries, trains a high-accuracy XGBoost model (94.7%), and deploys it on AWS SageMaker real-time endpoints for low-latency failure prediction. The system enhances reliability through scalable deployment and interactive dashboards. Tech Stack: Python, XGBoost, AWS SageMaker, Streamlit, Tinkercad, Pandas, NumPy, Scikit-learn 🔗 Visit Repo |
- Advanced NLP & LLMs (Training transformers with Hugging Face)
- Real-time Backend (Socket.io, Redis, WebSockets)
- System Design (Scalable architectures, Microservices)
- Deep Learning Specialization – Andrew Ng, Coursera
- Machine Learning with Python – IBM, Coursera
- System Design for Scale – Educative.io
💡 Fun Fact: I love optimizing algorithms & solving tricky problems! 😄