I’m a full-stack AI/Software engineer, building software and AI systems that’s meant to be scalable, reliable, and actually work (most of the time 😅).

I work across the stack—from clean, usable interfaces to solid backend systems—and I like turning ideas into end-to-end products that run in the real world.

Outside of work, I spend a lot of time experimenting with AI systems, distributed systems, and low-level code. I also contribute to open-source projects when something interesting catches my attention (which happens often enough that sleep becomes optional).

I’m especially interested in:

• Building full-stack systems that are simple but scalable
• Understanding how things work under the hood (OS, networking, memory)
• AI systems and practical applications of LLMs
• Low-level programming and performance-focused engineering
• Distributed systems and backend architecture

I tend to learn by building first, then figuring things out properly after.

That usually looks like:

• trying to build something before fully understanding it
• breaking it
• fixing it
• then rebuilding it better

It’s not always efficient, but it works.

Right now I’m exploring:

• systems programming (Rust, C)
• backend engineering at scale
• AI-powered systems and pipelines
• real-time and distributed systems design

• Languages: Rust, C, Python, Java, TypeScript
• Backend: FastAPI, Quarkus
• Systems: Linux, networking, concurrency
• AI: LLMs, RAG systems, inference workflows

I like:

• tinkering with low-level systems
• experimenting with AI ideas
• breaking things just to understand them
• building things that might not work on the first try (or second 😄)