I turn ideas into systems people can rely on.

I want to understand what happens underneath the abstraction, not simply learn how to call an API.

In practiceDigging into tensor memory layout, strides and training mechanics instead of stopping at the framework API.

A model is only one part of an AI product. I think about the complete path from user interaction and APIs through data, models, infrastructure and production behaviour.

In practiceMy AI projects span RAG and LangGraph workflows through databases, authentication, live event streams and the frontend.

I prefer understanding why a technology or algorithm is appropriate instead of memorising that it is the conventional choice.

In practiceLearning algorithms by their behaviour, complexity and trade-offs before their implementation patterns.

A prototype can demonstrate that something works. Production engineering asks whether it stays reliable when users, data, failures and edge cases appear.

In practiceChasing down duplicate detections, image orientation bugs, import/export mismatches and missing tests, not just the model.

The fastest way I internalise a concept is to use it to build something real.

In practiceFull-stack apps, then research systems, then ML engineering, LLM applications and AI agents.