I’m Michail, a Computer Science undergraduate at the University of Crete who enjoys building things at the intersection of conversational AI, data engineering, and applied machine learning. I like taking messy requirements, APIs, and datasets and turning them into something reliable: a VoiceAI call flow that actually works in production, a scraper plus ETL pipeline that keeps data clean, or an ML/RAG workflow that produces useful answers instead of noise.

My path so far has been quite hands-on. At Orama VR I volunteered as a UI/UX tester, running VR usability sessions and learning how small interaction details can completely change a user’s experience. At Conveos I moved closer to the data side: building a Node.js scraper, automating data cleaning and processing, applying ML for classification, and integrating datasets into a RAG system backed by LLMs. More recently at Omilia I focused on conversational AI in production environments – implementing client-specific VoiceAI logic, integrating external APIs into call flows, testing with tools like Postman and Zoiper5, and documenting features for other engineers.

Academically, my BSc in Computer Science and an Erasmus semester at the University of West Bohemia have given me a solid foundation in software engineering and algorithms, but I tend to learn the most when I have a concrete problem to solve. My default approach is to start small, prototype quickly, write down decisions and edge cases, and then harden things with tests and documentation so they’re easy for others to understand and extend.

Outside of work

Outside “official” projects, I like experimenting with new tools and workflows – small scripts to automate repetitive tasks, quick prototypes that explore a new API or model, or testing VR experiences and thinking about interaction patterns. I enjoy environments where people are curious, direct in their feedback, and open to sharing how they think about problems, because that’s where I learn the fastest.