3rd year Computer Science student at NTU with a passion for Machine Learning, Full-Stack Development, and Open Source.
- Currently building production-grade applications — from C++ networked systems to full-stack platforms and AI-powered tools.
- Working on open-source projects to strengthen skills in system design, distributed computing, and backend architecture.
- Ask me about C++, scalable backend systems, full-stack development, or how to integrate AI into production software.
- Reach me at raghav008@e.ntu.edu.sg or connect on LinkedIn
- Fun fact: As Vice-President of NTU Open Source Society, I’ve helped connect hundreds of students with open-source opportunities and guided them in contributing to real-world projects.
- Pursuing Bachelor of Engineering in Computer Science at Nanyang Technological University
- Prev. Data & Software Intern at Shopee, worked on optimizing internal reporting workflows and building autonomous agents to reduce costs.
- Prev. Machine Learning Intern at Singapore Airlines, worked on LLM fine-tuning and RAG systems
- Former Undergraduate Researcher focused on ML applications in education
- Passionate about building scalable systems and solving real-world problems with technology
- Implemented using socket programming, multi-threading, and ncurses for a terminal-based UI.
- Features concurrent client handling, message broadcasting, and graceful shutdown.
- Designed for scalability and modularity for future protocol extensions.
- Full-stack system to manage shared facility usage with transaction-safe booking logic to prevent double bookings.
- Backend: Node.js, Redis for locking & caching; Frontend: React.js.
- Deployed with Docker & Kubernetes for horizontal scalability.
- Developed an offline Retrieval-Augmented Generation system to assist engineers in retrieving technical documentation.
- Tech stack: PyTorch, Hugging Face Transformers, FAISS vector database.
- Optimized for secure, low-latency inference in air-gapped environments.
- ML-powered web tool to identify at-risk students with 88% accuracy.
- Tech stack: Random Forest, Neural Networks, Flask + React, Redis for async tasks, AWS Kubernetes deployment.
- LinkedIn: Raghav Gupta
- Email: raghav008@e.ntu.edu.sg
⭐️ From Raghav