- Contributions / last year
- 1,923
- Shipped to production
- 6 products
- Status
- Available for select work
Location
India 🇮🇳
Experience
3+ Years
Unclear state, leaky boundaries, a design system nobody trusts — that is what most “frontend” problems turn out to be. I start by making the data model and the component contracts obvious, and the screens follow.
- 01Model the data before the screenMost frontend problems are structure problems in disguise. I settle the data model and the component contracts first, so the screens come together fast — and still make sense six months later.
- 02Make the boundaries obviousState that lives in one place, components that own one job, and side effects you can point at. Boundaries are what let a second engineer land a change without reading the whole tree.
- 03Optimise the feedback loopFast local dev, honest types, and errors that say what went wrong. Cutting the loop from minutes to seconds changes what a team is willing to attempt.
- 04Ship it, then keep it legibleShipping is the start of maintenance. I leave behind naming, docs and tests that make the next change cheap rather than clever code that makes it expensive.
Own the frontend of Datachecks Enterprise — a data quality and observability platform where the interface has to stay readable across millions of rows.
- Built the comparison surface: automated schema, row and cell-level diffing with semantic-similarity matching, presented as side-by-side data grids that stay usable at scale.
- Shipped the validation and synthetic-data-generation workflows end to end, from the rule builder through to run history.
- Set the component contracts and design-system conventions the rest of the product now builds on.
From side projects to production systems — each one built with intent, iterated on with care, and shipped with pride.
NextJS
React
TailwindCSS
Docker
TypeScript
FastAPI
PostgreSQL
Prisma ORM
ShadCN
Flutter
Framer Motion
Tauri
Rust
OpenAPI
Actix Web
MongoDB
Open AI
Deepgram API
tRPC
Stripe
Python
Numpy
Pandas
Plotly
BitStamp API
Jupyter Notebook
Getting a real product on screen: typed end to end, componentised, and rendering fast on the first paint.
You get an interface that survives the roadmap
I build the boring structure well — data models, component contracts, state boundaries — so the next three features slot in instead of forcing a rewrite.
You get one engineer across the whole stack
React and TypeScript on the front, FastAPI, Node and Postgres behind it, Rust when it earns its place. Fewer handoffs, fewer things lost between them.
You get work that ships and stays shipped
Six products in production, maintained after launch. I care about the second year of a codebase as much as the first two weeks.
Everything I study gets written down and organised — system design, languages, DevOps, AI. Browse it the way I do: collection, chapter, section.
Networking & Communication
Explore the fundamentals of how systems communicate over a network, from basic client-server models to advanced globa...
Late nights, multiple monitors, and a terminal always open — this is where ideas become production-grade software. I care deeply about the details: clean architecture, fast feedback loops, and tools that don't get in the way.
241 hours of deliberate coursework, from Python fundamentals to LLM mechanisms. Each one a step toward being a sharper engineer, not a certificate collection.
- 01
The Complete Python Bootcamp: From Zero to Hero in Python
Jose Portilla · Pierian Training · 22 hours
Python 3NumPyPandasCompleted - 02
Python for Machine Learning & Data Science
Jose Portilla · Pierian Training · 44 hours
Scikit-LearnNumPyPandasCompleted - 03
A deep understanding of Deep Learning & Neural Networks with Python
Mike X Cohen · 57.5 hours
PyTorchGoogle ColabMatplotlibLearning - 04
A deep understanding of AI large language model mechanisms
Mike X Cohen · 91 hours
PyTorchHugging Face (Transformers/Datasets)TokenizersLearning - 05
Practical AI with Python and Reinforcement Learning
Jose Portilla · Pierian Training · 26.5 hours
TensorFlowKerasOpenAI Gym (Gymnasium)Learning
Available for select work. Data-heavy dashboards, design systems, or anything that has to stay legible at scale — those are the problems I like most. Two lines about the project is enough to start.