AI engineer. I build full-stack systems and think about how they break.
I don't just wire up models. I build AI systems that retrieve the right thing, fail loudly, and are worth trusting. From the first prototype to the last deploy, I care about the details that make software feel dependable and worth coming back to.
Build
- Models get the attention.
- Engineering makes them dependable.
- I combine both to build AI that holds up in production.
Break
- Fail loudly, never silently
- Security-minded by training
- Tested before it ships
- Edge cases hunted early
Trust
- Redis cache: 4s to 5ms
- CI/CD on AWS EC2
- CloudWatch alarms
- AES-256 encrypted storage
Breaking thingswas alwayspart of the build.
And from every break, I rebuilt it sharper.
From the First Prototype to the Final Deploy, Every Step Is Built to Create a DependableAI System.
Every project starts with a question, not an assumption. Through research, architecture and engineering, each stage is built to be measurable, secure and ready to ship.
Discovery
Every system starts with the problem. I dig into the data, the users and the constraints to find what actually needs to be built.
Architecture
Retrieval strategy, models, storage, auth. I design the shape of the system first, so the code has somewhere sensible to live.
Engineering
Agents, APIs and pipelines get built with tests, caching and security in mind. Every layer is made to be fast, observable and safe with data.
Deploy & Evolve
Shipping is only the beginning. CI/CD, monitoring and alarms keep it healthy, and real usage tells me what to improve next.
