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I'm a final-year BCA student building toward GenAI / LLM Application Engineering. I learn by building — working hands-on with LLM APIs (Gemini, Claude, Groq), local models (Ollama), Python fundamentals, and shipping small end-to-end AI-powered apps rather than just following tutorials.
Currently working through a structured 30-day GenAI learning plan and building toward larger projects: a RAG engine, a multi-agent system, and an LLM gateway with guardrails.
Looking for: internship or entry-level opportunities in GenAI/LLM application engineering.
RAG-powered document Q&A with cited, grounded answers
Upload a document and chat with it. Chunks and embeds the file locally, stores vectors in ChromaDB, and answers with Gemini 2.5 Flash, grounded in the retrieved content with source and page citations. If the answer isn't in the document, it says so.
Python Flask RAG ChromaDB sentence-transformers Gemini API
Anime episode tracker with LLM-powered recommendations
A full-stack Flask app that solves a real personal problem — losing track of what episode you're on. Add shows, bump episode counts, and get AI-generated "what to watch next" recommendations from Groq (gpt-oss-120b) based on your watchlist.
Python Flask Groq API LLM Integration Vanilla JS
AI / LLM
Tech Stack
Open to internships and entry-level opportunities in GenAI/LLM application engineering. Reach out via LinkedIn or email.