About Me
I’m a Tech Lead, currently focused on the architecture and evolution of large-scale production systems.
My background combines science (PhD in Analytical Chemistry), software architecture, and educational product development. I’m the author of the book iOS Architecture Patterns (Apress), which focuses on maintainable and scalable design. In recent years, I’ve been applying the same software engineering principles—reliability, observability, modularity, and long-term maintainability—to the challenges of AI systems in real-world production environments.
What I Focus On
My current areas of interest include: • RAG architectures and recovery systems. • Evaluation and observability strategies in AI pipelines. • Reliability and fault analysis in production systems. • Trade-offs of cost, latency, and scalability. • Practical application of established architectural patterns to AI systems.
How I Think About AI
I believe that most problems in production AI don’t stem from the model itself, but rather from the interaction between data pipelines, retrieval layers, evaluation, user flows, and real-world constraints.
My approach involves applying traditional software engineering rigor to build more predictable, maintainable, and monitorable systems, especially in educational contexts where reliability and human oversight are essential.
This Site
This site collects technical notes, architectural analyses, and reflections on building AI systems in production environments. The goal is to treat AI as what it is: a powerful tool that requires sound engineering to be truly useful and sustainable in the long run.
Follow the Work
I share short observations on architecture, system reliability, and lessons learned on LinkedIn and X. No hype. Just reflections from practice.
