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yoonus47/README.md

Hi there πŸ‘‹ I'm Yoonus!

AI Engineer | Data Scientist | Content Creator

Welcome to my profile. I am a Master of Artificial Intelligence graduate from RMIT University, based in Melbourne. I enjoy taking ideas from papers and turning them into working software. Checkout my YouTube Channel where I've made videos breaking down some of hottest topics in AI such as RAG (Retreival Augmented Generation).

Alot of my current work sits around RAG (Retrieval Augmented Generation), LLM agents, and deploying ML systems in a way that is practical.

πŸš€ About Me

  • πŸŽ“ Master of AI (RMIT University) + B.Tech in Computer Science
  • πŸ’Ό Ex AI Engineer at Gieom Business Solutions (built RAG systems for Tier-1 banks)
  • ⚽️ Football fan (still not soccer)
  • β™› Chess piece collector 😁 (around 1200 elo)

πŸ”₯ Currently Working On

⭐ AI Support Agent (Docs Deflection + Ticket Escalation)

Repo: https://github.com/yoonus47/AI_Support_Agent

AI Support Agent UI

A production-style support assistant that behaves like a real Tier-1 support rep:

  • Docs-first deflection: answers from internal docs whenever possible
  • Context-aware behavior: checks user plan (Free vs Pro/VIP) before escalating
  • Ticket escalation: creates a ticket only when needed, with priority based on rules

What makes this interesting for AI Engineering

  • Tool-using agent (LangChain) with structured tool inputs (Zod)
  • Backend API + Web UI (not just a CLI demo)
  • Built to be extensible for streaming, observability/tool traces, and RAG upgrades

Tech

  • Backend: Node.js, TypeScript, LangChain, Express, Groq (Llama 3.x 70B), Zod
  • Frontend: Next.js (App Router), TypeScript, TailwindCSS
  • DevOps: Docker/Compose support (optional)

πŸ’» Featured Projects

Tech: Python, OpenAI Whisper API, Hugging Face (BART), AWS EC2
AI web app that generates summarized lecture notes from audio recordings. Deployed on AWS EC2.

Tech: Python Flask, HTML/CSS, JavaScript
End-to-end ML web app that suggests potential medical conditions based on biometric inputs.

Tech: Scikit-learn, Pandas, AWS EC2
Regression model to predict housing prices in Bangalore, including feature engineering and deployment.

Tech: Flutter, Firebase
Cross-platform travel planning app with auth and real-time database integration.

πŸ› οΈ Tech Stack

AI & Machine Learning

  • LLMs and NLP: RAG, LangChain, OpenAI API, Hugging Face, prompt design
  • Frameworks: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy
  • Vector DBs: Chroma, FAISS, Elasticsearch

Data & Backend

  • Python, SQL, Java, C++
  • AWS (EC2), GCP (25+ badges), Docker, Git
  • MySQL, PostgreSQL, Firebase, MongoDB

Full Stack

  • React, Node.js, HTML, CSS, Flask
  • Flutter, React Native

πŸ“Ί Checkout my Youtube Channel where I make videos about AI

πŸ“« Connect with Me

I am open to AI Engineer and Data Scientist roles, collaborations, or just a chat.


Happy Coding! πŸš€

Pinned Loading

  1. AI_Support_Agent AI_Support_Agent Public

    TypeScript

  2. travelaza travelaza Public

    This repo contains files for the travelaza app

    Dart

  3. Medified Medified Public

    Jupyter Notebook

  4. lazzy.ai lazzy.ai Public

    This repository contains the code files for the Lecture Note Generator

    HTML

  5. Banglore-House-Price-Prediction Banglore-House-Price-Prediction Public

    AWS EC2 Instance

    Jupyter Notebook