MIT World Peace University, Pune
Bachelor of Science in Data Science.
About
B.Sc. Data Science student at MIT World Peace University, Pune. I care about systems that are correct, observable, and fast. I spend most of my time on backend architecture, API design, and the plumbing that makes full-stack applications not fall apart under load.
Outside of code I roam around on my motorcycle chasing sunsets and fast winds. I also like ricing my Arch Linux environment. These are my dotfiles.
Education
Work
Servzo (VendorGrid)
B2B Procurement platform digitising vendor management for small businesses. Live at vendorgrid.in. Engineered a high-concurrency monorepo backend with FastAPI, asyncpg, and SQLAlchemy 2.0. Features SSE-based real-time vendor notifications, HTTP-only cookie auth, structured JSON logging, and a Docker Compose deployment behind Nginx on a DigitalOcean VPS.
AWS Cloud Club — MIT-WPU
Designed and implemented a genetic algorithm timetable scheduler in Python, optimising a multi-constraint fitness function across 2,000 evolutionary iterations. Authored a CLI automation toolkit leveraging Python asyncio
for concurrent task orchestration.
Projects
Watchtower
Real-time observability dashboard built on a hot/cold path architecture. Hot path: Redis Pub/Sub fans out events to SSE subscribers via Elysia (Bun). Cold path: BullMQ workers drain into ClickHouse for long-term retention and analytical queries. Frontend in TanStack Start (React) with TanStack Query, Eden Treaty for end-to-end type inference across the monorepo.
Vriddhi
Self-hosted NSE end-of-day data pipeline, archive, and screener. Downloads and structures twenty years of market data into a queryable PostgreSQL archive. Exposes a FastAPI REST API consumed by a React dashboard. Built with an async-first architecture using Python, asyncpg, and a Redis-backed task queue.
MarketMiner
Python library for financial forecasting and portfolio optimisation using
NSE end-of-day index data. Combines statistical analysis and machine learning
with a Progressive Web App dashboard. Rebuilt from a family member's original
pipeline with strict architectural patterns: one-way dependency flow,
StrEnum
throughout, injectable transports for testing.
Stack
Languages: Python (Advanced), TypeScript, Rust, C/C++, SQL
Backend: FastAPI, asyncio, asyncpg, SQLAlchemy 2.0, BullMQ, Elysia (Bun), Redis, ClickHouse
Frontend: React, TanStack (Query / Router / Start), Zustand, Pydantic v2
Infra: Docker Compose, Nginx, Let's Encrypt, DigitalOcean, AWS, Git, Arch Linux / Sway
Currently learning: deeper systems programming in C++, ClickHouse internals
Writing
I take notes while I build — on architecture decisions, things that surprised me, and things I had to understand properly before I could move on.
Moving to homelab infra made things 4x slower, and I blamed the wrong thing
I built Watchtower, a self hosted log ingestion pipeline. Splitting the
infra across two machines made throughput four times worse, and I spent
way too long blaming the hardware before tracing it down to a single
await
keeping every request blocked on a network round trip.