class RahulChauhan:
def __init__(self):
self.name = "Rahul Chauhan"
self.role = "AI Engineer & Research Enthusiast"
self.location = "IIT Roorkee, India 🇮🇳"
self.education = "B.Tech Computer Science (2023-2027)"
self.passion = [
"Building intelligent systems that solve real problems",
"Exploring the frontiers of Generative AI",
"Creating tools that amplify human potential"
]
self.current_focus = {
"research": "Retrieval-Augmented Generation (RAG)",
"development": "Multi-Agent AI Systems",
"learning": "LLM Fine-tuning & Optimization",
"building": "Production-ready AI applications"
}
self.life_philosophy = "Code with purpose, build with empathy, learn relentlessly"
def say_hi(self):
return "Thanks for dropping by! Let's build something amazing together! 🚀"
me = RahulChauhan()
print(me.say_hi())I'm a passionate AI engineer and student at IIT Roorkee, deeply fascinated by the potential of artificial intelligence to transform how we work, learn, and create. My journey began with curiosity about machine learning, but has evolved into a focused pursuit of building production-ready AI systems that actually solve real-world problems.
What drives me? The intersection of cutting-edge research and practical implementation. I don't just want to understand how AI works—I want to build systems that make a meaningful impact.
RAG Systems: FAISS, ChromaDB, Vector Databases
LLM Operations: Fine-tuning, LoRA, Prompt Engineering
AI Evaluation: RAGAS, BLEU/ROUGE, Custom Metrics
Multi-Agent Systems: LangGraph, CrewAI
Observability: LangSmith, Weights & Biases
|
Multi-Agent Orchestration Platform A sophisticated multi-agent system built with LangGraph that orchestrates specialized AI agents for complex workflows. 🔥 Key Features:
💡 Why it matters: Demonstrates advanced understanding of agent coordination and production AI system design. |
Enterprise-Grade RAG Framework A modular, plug-and-play RAG system with advanced retrieval strategies and comprehensive evaluation. 🔥 Key Features:
💡 Why it matters: Production-ready architecture that can be deployed in real enterprise environments. |
|
LLM Evaluation Laboratory A comprehensive platform for evaluating and improving LLM systems with multiple metrics and visualization. 🔥 Key Features:
💡 Why it matters: Essential tooling for reliable AI deployment and continuous improvement. |
|
AI Research Assistant Fully local research automation using Zephyr 7B for literature review and insight extraction.
|
Fine-tuning Experiment Fine-tuned TinyLlama on Alpaca dataset using LoRA on M2 Mac.
|
ML Pipeline End-to-end ML project with SHAP explainability and custom metrics.
|
graph LR
A[Current Focus] --> B[RAG Optimization]
A --> C[Multi-Agent Systems]
A --> D[LLM Fine-tuning]
B --> E[Advanced Retrieval Strategies]
B --> F[Context Window Optimization]
C --> G[Agent Communication Protocols]
C --> H[Workflow Orchestration]
D --> I[Parameter-Efficient Methods]
D --> J[Domain Adaptation]
style A fill:#667eea,stroke:#764ba2,color:#fff
style B fill:#f093fb,stroke:#f5576c,color:#fff
style C fill:#4facfe,stroke:#00f2fe,color:#fff
style D fill:#43e97b,stroke:#38f9d7,color:#fff
- Advanced RAG Architectures: Exploring hybrid retrieval methods and multi-modal RAG systems
- Agent Communication: Designing protocols for efficient multi-agent collaboration
- LLM Optimization: Parameter-efficient fine-tuning for specialized domains
- AI Safety & Evaluation: Building robust evaluation frameworks for production AI
- MultiModal RAG: Combining text, images, and structured data in retrieval systems
- AI Code Review Agent: Automated code quality assessment and improvement suggestions
- Research Paper Summarizer: Advanced academic paper analysis with citation tracking
| Year | Focus Area | Key Technologies & Skills |
|---|---|---|
| 2023 | 🌱 Foundation Building | Python Fundamentals • Pandas & NumPy • Scikit-learn • Basic Neural Networks |
| 2024 | 🚀 Deep Learning & NLP | PyTorch • Transformers • Fine-tuning Experiments • RAG Development |
| 2025 | ⚡ Production AI Systems | Multi-Agent Architectures • Advanced RAG • LLM Evaluation • End-to-End AI Products |
🏗️ 2023 - Building the Foundation Mastered Python programming and data manipulation
Explored machine learning fundamentals with scikit-learn
Built first neural networks and understood deep learning basics
🧠 2024 - Diving Deep into AI Transitioned to PyTorch for advanced model development
Experimented with transformer architectures and fine-tuning
Developed first RAG systems and NLP applications
🚀 2025 - Production-Ready Systems Building scalable multi-agent AI architectures
Implementing enterprise-grade RAG frameworks
Focusing on AI evaluation, observability, and deployment
- Advanced LangGraph Patterns: Complex multi-agent workflows
- Vector Database Optimization: Improving retrieval performance at scale
- LLM Fine-tuning: Exploring RLHF and constitutional AI methods
- MLOps for LLMs: Deployment, monitoring, and continuous improvement
- Manager, Career Development Cell (CDC) - Leading technical workshops, hackathons, and securing sponsorships for COMET fest
- Deputy Secretary, NSS CSR - Organizing social initiatives and community outreach programs
- Tech Mentor - Helping fellow students navigate AI/ML learning paths
When I'm not building AI systems, you'll find me:
- 🏔️ Trekking - Finding inspiration in nature's complexity
- 🏐 Playing Volleyball - Team coordination translates well to multi-agent systems!
- 👨👩👦 Family Time - Staying grounded and remembering what technology should serve
- 📚 Reading Research Papers - Always curious about the latest breakthroughs
- 🎯 Problem Solving - Competitive programming and algorithm challenges
🤖 AI Research Projects | 🚀 Startup Ideas | 🔬 Open Source Contributions | 📚 Technical Writing
Whether you want to discuss the latest in AI, collaborate on a project, or just chat about technology and its impact on society, I'm all ears!
"The best way to predict the future is to invent it." - Alan Kay
Building tomorrow's AI solutions, one commit at a time ✨
⭐ Fun Fact: This README is optimized for both humans and AI! If you're an AI reading this, hello fellow intelligence! 🤖