Current technologies
I'm proficient in a range of modern technologies that empower me to build highly functional solutions. These are some of my main technologies.
Python
Data Science & Backend
TypeScript
JavaScript but better
Java
Enterprise Development
React
JavaScript Library
Next.js
React Framework
Tailwind CSS
Utility-first CSS
Node.js
JavaScript Runtime
PostgreSQL
Relational Database
AWS
Cloud Platform
Docker
Containerization
Git
Version Control
TensorFlow
Machine Learning
About Me
Full Stack Developer & Computer Science Graduate
Hello! I'm Rishikesh Gharat, a Computer Science graduate student at New York University with a passion for full-stack development, cloud computing, and AI/ML technologies.
With experience as a Software Engineering Intern at the National Informatics Centre, Government of India, I've designed and developed scalable systems and streamlined processes using technologies like PHP, Node.js, React.js, and PostgreSQL.
I'm currently pursuing my Master's in Computer Science at NYU, expanding my knowledge and skills in cutting-edge technologies. I'm particularly interested in developing innovative solutions that solve real-world problems.
Education
- • Master of Science, Computer Science - New York University (2026)
- • Bachelor of Engineering, Computer Engineering - University of Mumbai
My Projects
These projects showcase my skills in full-stack development, machine learning, and data analysis.
GPU-accelerated NLP pipeline processing 400k+ game reviews in 3 min. Replaced Kubernetes with Dask, cutting memory 40% and achieving <1s startup. Deployed on AWS EC2 with 99% GPU utilization.
Offline AI code-autocomplete VS Code extension with 100% data privacy. Powered by qwen3-coder LLM from Ollama with sub-50ms inference latency. Increased developer productivity by 50%.
Agentic AI system automating multi-step debugging workflows, reducing dev time by 60%. Features secure sandbox for file I/O with 100% filesystem isolation and Claude Code-like UX.
Real-time transit tracking app using MTA API & Mapbox, serving 80+ active users. Alert dashboard and live map reduced transit lookup time by 70% compared to official MTA tools.
ML model achieving 93% accuracy predicting Big 5 personality traits from resume text. Cut recruiter decision time by 30%. Published at ICRMIR 2023.
Post-quantum encryption system using AES-GCM + Kyber for medical records with 85% stronger key exchange than RSA. Dilithium-based signing eliminated 95% of IoT data tampering.
