About

My work spans hardware and software engineering, data systems, machine learning, and applied research. One question connects these areas: what does it take to move an idea beyond a prototype and make it work reliably in the real world?

Professional background

My technical background began in hardware and network engineering before expanding into software engineering, data-intensive applications, and production systems. This experience taught me to approach reliability across the entire system—from infrastructure and data pipelines to applications and models.

Today, I work as a technology lead focused on large-scale machine-learning and intelligent data systems. My experience includes business entity resolution, embeddings, vector search, LLM-assisted workflows, model evaluation, and the data platforms that support production ML.

I focus on the engineering decisions that determine whether machine learning remains reliable after deployment: data quality, orchestration, scalability, observability, governance, and measurable evaluation. Earlier experience in business and international operations also helps me connect technical decisions with operational outcomes.

Research background

Alongside my professional work, I conducted doctoral research in mathematical modeling, numerical methods, and intelligent monitoring systems.

My dissertation examines anomaly detection in photovoltaic systems using operational telemetry. The research combines physics-informed digital twins, statistical analysis, and autoencoder neural networks to identify degradation and unusual behavior at the level of individual solar panels.

This work continues to shape how I approach technical problems: model individual components, account for operating conditions, study long-term behavior, and combine multiple methods rather than relying on a single signal.

Current interests

My current work and independent research focus on several connected areas:

I write about technical approaches, engineering trade-offs, and lessons that remain useful beyond demonstrations and controlled environments.