III Year — Undergrad
VARUN KUMAR
Artificial Intelligence · Deep Learning · Agentic Systems
Building intelligent systems that think, learn, and adapt.
From multi-agent architectures to deep learning pipelines —
I engineer the future of AI, one model at a time.
⚡ Languages & Tools
Python
Java
SQL
MongoDB
PostgreSQL
MySQL
Redis
Qdrant
Docker
Git
GitHUB
Competitive Programming
Neo4j
LangChain
🧠 AI & Machine Learning
Machine Learning
Deep Learning
Generative-AI
RAG
Agentic RAG
Graph-RAG
Fine-Tuning
Agentic AI
LLM
Multi-Agents
Google Multi-Agent Modules
🏆
Synaptix AI Hackathon — IIT Madras
Finalist
💻
Competitive Programming — IIT Madras
Finalist
🚀
TATA Imagination Challenge
Semi-Finalist
Designed and simulated an AI-powered Hospital IVR system replacing legacy DTMF navigation with a
hybrid ILP-AI architecture for automated symptom triage and emergency ambulance dispatch.
Architected a hybrid ILP + SLM pipeline using Gemma 3:4B for
clinical reasoning and SCIP for optimal ambulance-to-patient assignment
Designed an NLU pipeline with speech-to-text, intent
detection, distress classification, and clinical NER for multi-language scenarios
Simulated a microservices backend (NestJS + Python) with
PostgreSQL and Redis, handling 1,000+ concurrent call scenarios at 99.9% uptime
Gemma 3:4B
NestJS
PostgreSQL
Redis
HL7/FHIR
Built an AI-powered skill intelligence platform to enrich labor market data using knowledge
graphs and LLMs.
Constructed a Neo4j knowledge graph with 2,800+ skill nodes
and 768-dim embeddings capturing skill taxonomy hierarchies
Developed a job description enhancement pipeline using Gemini
1.5 Pro with few-shot prompting and chain-of-thought reasoning
Trained a Graph Neural Network (GNN) with Node2Vec embeddings
for skill classification and relationship inference
Neo4j
Gemini 1.5 Pro
GNN
spaCy
Node2Vec
Worked on building a scalable AI-powered document query system for enterprise users, enhancing
document search and retrieval using NLP and vector search technologies.
Developed a full-stack RAG system using FastAPI, Streamlit,
MongoDB, Qdrant, and Hugging Face Transformers
Implemented real-time semantic search across multiple PDFs
with support for voice-based queries
Improved document search speed by 80% with enterprise-grade
concurrent request handling
FastAPI
Streamlit
MongoDB
Qdrant
Hugging Face
Contributed to the end-to-end development of a Restaurant Management System tailored for a local
business client, focusing on order processing, menu management, and billing features.
Developed the backend using Java and MySQL, and built user
interfaces with Java Swing
Designed modules for order tracking, table reservation, and
inventory management
Delivered the solution with 100% on-time delivery and zero
post-deployment bugs
Java
MySQL
Java Swing
SDLC
✦
VOID LOG
Thoughts, experiments, and dispatches from the infinite void.
Enter the Void Log →
