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READYSYS

About me

Building Machines that
Understand the World.

"I build and ship end-to-end AI-powered systems - from computer vision pipelines to RAG-based NLP architectures."

Currently an M.S. CS candidate at Rutgers University, I specialize in ML, NLP, and AI engineering. My work bridges the gap between complex research and production-ready applications, leveraging everything from YOLOv8 and ByteTrack to Gemini API and Supabase.

GEOLOCATION SYSTEM

New Jersey, USA

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LAT

40.7128° N

LONG

74.0060° W

Education

MSCS @ Rutgers

Major in AI • GPA: 3.50

Scientific Recognition

01 Patent Recognized

IP INDIA • 2025

My Journey

B.Tech in Internet of Things

Dec 2021 - May 2025

Built strong foundations in algorithms and IoT systems. Achieved a GPA of 3.67 while pioneering student technical initiatives.

Artificial Intelligence Intern

Apr - June 2024

Developed and optimized ML models for production; built modular Python preprocessing and feature engineering pipelines.

AI Research Intern

2023 - 2025

Conducting AI/ML research at the Indian Meteorological Department - led development of an Attention-LSTM weather forecasting system, recognized as a government patent (IP India, 2025).

M.S. in Computer Science

Sept 2025 - Present

Specializing in ML, NLP, and AI Engineering. Current GPA: 3.50. Building RAG pipelines and end-to-end AI systems.

My Skills

A comprehensive overview of the technologies and tools I use to build intelligent systems.

Featured Projects

01
COMPUTER VISION

Vtrack

Multi-class object detection pipeline (YOLOv8, ByteTrack) achieving 25-30 FPS with real-time speed estimation and 3D dashboard integration.

YOLOv8BYTETRACKFASTAPIR3FSUPABASE
Vtrack
02
AI ASSISTANT

SkillGap

AI career simulator using Gemini 2.5 Flash for multi-turn technical interviews and structured performance evaluation based on resume-job gaps.

NEXT.JS 15GEMINI 2.5SUPABASEPDF.JS
SkillGap
03
NLP / RAG

DocPilot

RAG pipeline for millisecond-latency Q&A over 500+ documentation pages, utilizing Llama 3.1 and ChromaDB for hallucination-reduced inference.

LLAMA 3.1CHROMADBHUGGINGFACESTREAMLIT
DocPilot
04
QUANT FINANCE

QuantVision

Backtesting pipeline that generates trading signals, evaluates cumulative returns vs buy-and-hold, and tunes parameters for strategy optimization across historical price data.

PYTHONPANDASNUMPYMATPLOTLIBJUPYTER
QuantVision

Can You Beat My Agent?

Experience reinforcement learning in action. Survival is the metric - interception is the agent's only goal.

Simulation Time

0.00s

Sector Record

0.00s

Threat Level

Low

Can You Beat My Agent?

Testing predictive pursuit algorithms in a contracting spatial arena. Agent difficulty escalates exponentially after 10s.