About Me
I'm a grad student at Arizona State University pursuing my MS in Computer Science with a perfect 4.0 GPA. I did my undergrad in AI & Data Science from the University of Mumbai, and since then I've been hooked on building things that sit at the intersection of software engineering and machine learning.
Currently, I work as a Research Aide at ASU, where I build production FastAPI microservices with RAG pipelines using LangChain and FAISS. Outside of work, I love shipping side projects — from a referral platform with 1,800+ users to an ML-powered water footprint tracker downloaded in 50+ countries. I get a kick out of taking an idea from zero to production and seeing real people use it.
MS in Computer Science
Arizona State University
GPA: 4.0/4.0
Based in Tempe, AZ
Originally from Mumbai, India
Research Aide @ ASU
Building RAG pipelines & microservices
Full-Stack + AI/ML
From Flutter apps to ML models
GitHub Contributions
Technical Skills
Experience
- Engineered FastAPI-based backend services with RAG-enhanced LLM routing, implementing AWS-deployed ML pipelines and CI/CD workflows for research query processing
- Implemented chat-history retrieval system integrated with Supabase, injecting conversational context into LLM calls to improve response relevance across research experimentation
- Architected cross-platform mobile application using Flutter with Supabase backend, integrating secure auth and real-time data sync to serve 500+ rural women with health education
- Designed accessibility-first UI with multi-language support and voice navigation across 15 app screens, collaborating with designers through iterative testing
- Developed RESTful APIs using Django, employing caching and query optimization to achieve 60ms response time for ERP dashboard operations
- Led initiative to restructure PostgreSQL schema across 15+ enterprise tables, deploying indexing strategies to reduce latency for high-volume data operations
Featured Projects
Referrlyy
Referral networking platform connecting job seekers with employees at top companies. Built cross-platform mobile app and web portal for seamless referral management.
Technical Highlight
Built end-to-end referral workflow with PostgreSQL for data persistence, Node.js/Express for API layer with request validation middleware, and Flutter using BLoC pattern for state management. Deployed on Render with automated CI/CD pipelines.
BoltPrep
AI-powered mock interview app with real-time speech-to-text, intelligent answer evaluation, and personalized feedback to help users ace their interviews.
Technical Highlight
Engineered low-latency audio pipeline combining Deepgram streaming STT with Gemini API for contextual answer evaluation. Used Riverpod for state management with caching strategies to minimize API calls while maintaining real-time responsiveness.
Aqua Trace
ML-powered water footprint calculator that uses image recognition to identify food items and track daily water consumption with environmental impact insights.
Technical Highlight
Applied transfer learning on MobileNet V2 with custom classification head, using data augmentation techniques to achieve 92% accuracy. Converted model to TFLite with quantization for efficient mobile inference under 100ms.
Mili
AI mental health companion built at SunHacks 2024 featuring voice-enabled conversations, mood tracking, and personalized wellness insights.
Technical Highlight
Designed efficient context management by chunking conversations into summaries before LLM calls, reducing token consumption by 60% while preserving conversational continuity. Built mood tracking with PostgreSQL time-series queries for trend analysis.
Auto EDA
Automated exploratory data analysis tool that generates comprehensive visualizations, statistical summaries, and insights from any dataset with minimal configuration.
Technical Highlight
Built modular analysis pipeline using Pandas for data profiling, automatically detecting numeric/categorical columns and applying appropriate statistical tests. Implemented Streamlit caching for efficient handling of large datasets.
