I'm Rayhan Vora — a B.Tech student in Artificial Intelligence & Data Science at L.J. University, Ahmedabad (Class of 2028), with a prior Diploma in Computer Engineering. I came into AI from a full-stack background, so I care as much about shipping a model as training one.

I build GenAI and RAG applications end to end — document ingestion, chunking and embeddings, vector search with FAISS, orchestration with LangChain / LangGraph, and delivery through FastAPI, Flask or Django with a React frontend.

I've interned as an AI/ML Intern at BrainyBeam Technologies and a Python–Django Intern at Creart, and I'm Oracle Cloud Infrastructure 2025 AI Foundations Associate certified.

📌 Currently open to AI/ML, GenAI and Data Science internships.

• 🎥 YouTube Chat GenAI — RAG over video transcripts with Hindi/English detection and strictly scoped answers (Flask · React · MongoDB · LangChain · FAISS)
• 🤖 Multi-Agent Workflow Automation — task-routing agents on a state graph (LangGraph · FastAPI · Celery · Redis · React)
• 🧠 RAG Chatbot — upload PDF / DOCX / TXT and chat with an AI that answers strictly from your own files, every fact cited back to the page it came from; scanned pages are OCR'd by a Groq vision model, then chunked, embedded and served from a per-user FAISS index (Django REST Framework · MongoDB · FAISS · ONNX all-MiniLM-L6-v2 embeddings · Groq gpt-oss-120b · React · Vite · Tailwind · JWT)
• 📬 Smart Email Agent — Gmail-connected AI copilot that summarises the inbox, drafts replies in 13 styles and runs streamed multi-step agent commands; spam and phishing are trained scikit-learn classifiers (0.986 / 0.989 F1) rather than prompts, and nothing leaves the outbox without a human click (FastAPI · React · TypeScript · Tailwind · Supabase · Gmail API OAuth 2.0 · Mistral AI · scikit-learn)

• 🧠 Agent orchestration — LangGraph state machines, tool use, memory
• 🔍 Retrieval quality — chunking strategies, hybrid search, re-ranking
• 📊 Data Science foundations — statistics, feature engineering, model evaluation
• ⚙️ MLOps basics — Docker, experiment tracking, deploying models as APIs
• 💻 DSA in Python — building consistency for interviews