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
- Built production FastAPI microservices with RAG pipeline using LangChain and FAISS vector database, deployed on AWS Lambda with CI/CD via GitHub Actions to handle 500+ concurrent queries daily
- Engineered chat-history retrieval system with PostgreSQL backend implementing connection pooling and Redis caching layer, reducing average query latency from 800ms to 320ms (60% improvement)
- Created cross-platform mobile application using Flutter and Supabase, implementing OAuth 2.0 authentication with Google Sign-In and real-time data synchronization serving 300+ users across rural India
- Designed accessible UI with multi-language support (English, Hindi, Marathi) and voice navigation using Flutter TTS/STT, reducing onboarding completion time from 8 minutes to 5 minutes (40% improvement)
- Developed RESTful APIs for ERP system using Django REST Framework with Redis caching and Celery for async task processing, handling 50K+ daily requests with average response time of 60ms
- Optimized PostgreSQL queries by adding composite indices on frequently-joined columns and refactoring Django ORM queries to use select_related/prefetch_related, reducing report generation time from 45 seconds to 6 seconds
Featured Projects
ADAPT-SQL
State-of-the-art Text-to-SQL system achieving 93.7% execution accuracy on the Spider benchmark using fully local LLM processing, outperforming GPT-4 based approaches like DAIL-SQL and DIN-SQL.
Technical Highlight
11-step pipeline spanning schema linking, complexity classification, similarity search, adaptive SQL generation, and validation-feedback retry. Achieves 93.7% execution accuracy on Spider 1.0 using fully local Qwen3-Coder via Ollama — no API costs, outperforming GPT-4 based DAIL-SQL (86.6%) by 7.1 points.
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.
