Welcome! I'm Aaryan, a sophomore studying Computer Science at the University of Waterloo. Currently, I'm joining Xsolla as an AI Engineer this Fall, where I'll be working on the payments infrastructure behind Roblox, Epic Games, Rockstar Games, Valve, Twitch and others. Previously, I was an AI Full-Stack Engineer at Decimal Technologies, where I built LLM agent pipelines with LangGraph and LangChain to automate multi-step workflows, and RAG systems grounded in live knowledge bases using pgvector and PostgreSQL.
On campus I'm at present a part of the Chassis Design Team for UW Formula Electric and have served as a member of the Undergraduate Residence Advisory Committee.
Outside of code my interests lie in Chess, Motorsport, Gaming and 3D-Design.
I am currently exploring the fields of Visual Intelligence, Hardware Development, Machine Learning, and Video Editing.
A production AI agent system built on LangGraph, structuring multi-step reasoning as task graphs with defined triggers and routing logic, backed by a full RAG pipeline grounding LLM outputs in structured evidence and deployed on AWS with Docker and CI/CD.
A computer-vision system that measures hand-luggage dimensions from a single camera and flags oversized bags against airline cabin limits, with three swappable dimension-estimation backends and a confidence-aware compliance model that refuses to fail a bag it isn't sure about.
A full-stack, AI-native application that turns a Spotify library into something you can converse with, asking questions in plain language and getting answers grounded in your own listening history.
An NLP research analytics system that processes arXiv abstracts at scale, using sentence embeddings and similarity search to index, retrieve and score papers for originality.
A productivity platform folding tasks, habits and calendar into one surface, driven by natural-language conversation with an assistant rather than forms.
A healthcare screening tool pairing symptom analysis with computer vision to identify skin conditions, optimised for edge deployment on Raspberry Pi for low-resource settings.