I write code, lead teams, and make complex systems work in production.
Most of my time goes to coding and aligning engineering, product, and leadership − turning ambitious ideas into technology that scales and stays secure. I'm hands-on with GenAI, focusing on practical applications that improve throughput and reduce risk.
Architecture: Maintainable systems that still make sense a year from now.
Delivery: Predictable shipping with minimal ceremony.
Operations and security: Scaling without breaking things. Security that doesn't get in the way. Infrastructure that supports growth instead of fighting it.
GenAI: LLM observability, tracing, evaluation, and cost management − built for production realities.
- Languages: Python, Ruby, Go, TypeScript/JavaScript
- Cloud and infra: AWS, GCP, Kubernetes, Docker, Terraform
- GenAI: LLM integration, RAG, AI-assisted workflows
Building tools for LLM observability and cost tracking. Exploring how GenAI can improve development workflows without sacrificing code quality or security.
- EgressLens − monitor outbound network activity from Python apps running in Docker.
- LLM Observability PoC − a Langfuse-based PoC for LLM observability in Python.
- ESP32 Kobo Page Turner − ESP32-S3 BLE HID page-turner for Kobo e-readers.
When not writing software, I'm building things on a breadboard. ESP32 firmware, sensors, basic circuits − the kind of stuff that reminds you why hardware is hard 👷