- 🔭 Building agentic AI systems using LangGraph, LangChain, and custom tools
- 🌱 Deepening my understanding of Machine Learning and Deep Learning from scratch
- 💡 Passionate about real-world AI applications: image-to-text analysis, RAG, and intelligent agents
- 🎓 Studying AI Engineering,spending most of my time coding AI & web projects
- 🚀 Actively seeking internships in Artificial Intelligence or AI-powered Web Development
| Area | Technologies & Frameworks |
|---|---|
| 🤖 Agentic AI | LangGraph, LangChain, Agent Workflows, Tool Use, Multi-Agent Coordination |
| 🧠 Machine Learning | Python, Pandas, Scikit-learn, Unsupervised Learning, Model Evaluation |
| 딥 Deep Learning | PyTorch, Neural Networks, Embeddings, Transfer Learning |
| 📚 LLM & RAG | Retrieval-Augmented Generation (RAG), Vector Databases, Prompt Engineering |
| 🖼️ Vision + Text | Image-to-Text Extraction, OCR (PaddleOCR), Cloudinary for media handling |
| 🌐 Full-Stack (AI Apps) | Next.js, React, FastAPI, REST APIs, Docker, Custom Auth |
| 🗄️ Data & Storage | PostgreSQL, Vector DBs (LanceDB), JSON, Environment Management (.env) |
I focus on understanding how things work — not just copying code. Every project is a step toward mastering AI.
- ✅ A multi-agent system that analyzes uploaded images (even blurry text!) and gives detailed, grounded answers
- ✅ Integrating Cloudinary + LangChain to build an AI assistant that reasons over user-uploaded media
- ✅ Experimenting with knowledge bases: When to use vector DBs vs direct LLM context?
- ✅ Building a Next.js + Python backend fully containerized with Docker for AI services
I’m always open to collaborations, learning opportunities, or a quick chat about agents, embeddings, or end-to-end AI apps!
If you’re building something cool in AI, ML, or intelligent web apps, say hello!
“Teach the machine, but never stop learning yourself.” 🤖✨