I'm a Software Engineer with experience building backend services, automation systems, web applications and machine-learning solutions.
At Schneider Electric, my work has included QA automation, backend development, automated testing frameworks, process optimization and software engineering across product teams. My primary development stack includes Python, TypeScript, JavaScript, React and Node.js, with additional experience in Java, machine learning and data-driven application development.
I also have a research background in applied machine learning. My work on vehicle fuel-consumption and CO₂-emission prediction was published in the proceedings of the 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN), where multiple regression, ensemble and neural-network approaches were evaluated through a Flask-based prediction application.
I enjoy solving engineering problems that sit at the intersection of software systems, automation and intelligent applications.