About Me

Currently in my 8th sem, trying to navigate this beautiful mess of tech, design, and everything in between. I enjoy experimenting, learning by trying, building, failing, re-learning.

I've interned at a YC-backed startup as a data analyst — started with UI/UX, but curiosity (and a Python-heavy resume) got me playing with Databricks, KPIs, and EV fleet data. They offered me a 6-month Data Science internship afterward, but life happened.

I've worked on classic ML problems — multi-class classification, regression — and dabbled with EfficientNetV2B3 to classify ocular diseases from fundus images (DL got real there).

Recently, I've been building with local and API-based LLMs — stuff like:

• Got PDFs of energy usage? I'll give you carbon emissions (Ollama Phi + ChromaDB + mxbai + HF Transformers).
• Training LLMs to spit GTM strategies and test marketing/sales ideas — try it here, because GPT ≠ only chat.

Also attempted brain segmentation with MONAI and nnUNet (tumors, lesions) using GCP VMs, learned how painful it is to make use of free credits and how painful T4s can be to configure.

I like design, love tech, and I kinda type fast (100wpm flex), think in flows, and build proof-of-concepts over Leetcode solutions. I know that's not the conventional route, but I've learned to build things that make sense to people — and sometimes that's enough.

Open to data science / MLE / AI engineer roles — or anything that mixes data, product thinking, and some taste.

This site? Just me — learning, building, failing publicly.

If something here resonates or you're building something cool — say hi.