After more than seven years building customer-facing and production software, I am moving deeper into data science, machine learning, Generative AI, and LLM-based systems. The domain is new, but many of the engineering questions are familiar: reliability, interfaces, observability, validation, and useful outcomes.
I am approaching this transition by strengthening the foundations first, then connecting them to practical systems—retrieval, evaluation, intelligent agents, and deployment workflows that can be understood and improved.
Production experience is part of the foundation
Working on checkout journeys, analytics integrations, automated validation, and data-backed applications taught me that a system is only useful when it behaves dependably under real constraints. AI products raise the same bar while adding new uncertainty.
My longer-term interests include human-AI interaction, computational approaches to cognition, and AI-assisted decision systems. The immediate goal is more concrete: learn to build and evaluate capable AI systems on top of rigorous software-engineering practice.