I help teams deploy AI that lasts.

Amsterdam, NL

Technical leader for the scientific aspects of AI Agents.

• Designed and built a system to power AI agents with Long Term Memory capabilities.
• Researched and implemented test-time compute techniques for AI agents.
• Wrote and promoted development guidelines for AI agents.
• Co-designed adn developed an evaluation framework for GenAI applications used to assess all the GenAI production systems across the org.
• Built an in-house agentic Text-To-SQL system that helped to augment the knowledge base of a production LLM and improve the relevance of its context up to 50%.

Amsterdam, NL

Technical leader of Machine Learning Excellence, improving ML quality and standards across the organization.

• Led ML excellence initiatives to improve quality and maintainability of ML systems. Supervised a working group with a team of 10+ ML scientists and engineers, including seniors and managers.
• Supported the creation, launch and adoption of an AI Governance framework across the organization.
• Reviewed, tested and supported the adoption of 3rd party AI governance tooling.
• Built, launched and promoted an ML Quality & Maturity framework that helps practitioners deliver high-quality systems with less effort.
• Documented and promoted ML best practices (e.g., CI/CD, retraining schemes, monitoring, fairness, ownership).

Amsterdam, NL

Built end-to-end ML systems in Customer Service ML, focusing on cost reduction, efficiency, and enhanced customer experience.

• Built and productionized ML systems end-to-end (problem formulation → data pipelines → modeling → deployment → monitoring → retraining).
• Led the development of ML Quality Guidelines & Best Practices in Customer Service ML, which resulted in significant increase of code quality (such as unit testing, unified code style) in the department’s Git repositories.
• Led the research and development of an in-house Python library for bias detection & mitigation (ML Fairness).

Amsterdam, NL

Started my ML journey at Booking.com, working on predicting customer loyalty using large-scale data.

• Delivered an end-to-end customer loyalty report and identified the main loyalty drivers that helped optimize workforce management.
• Applied ML at scale using techniques such as Linear/Poisson Regression, Random Forest, Gradient Boosting Machines and frameworks like H2O and MLlib for distributed ML.
• Extracted and manipulated data in the Hadoop ecosystem using Hive and Apache Spark.

Amsterdam, NL

• Interdisciplinary MSc combining data science, business optimization, and finance.
• Focused on machine learning, deep learning, data mining, and applied optimization—translating business problems into measurable models.
• Tools: Python/R, TensorFlow/Keras; Spark/Hive/SQL; MATLAB/VBA; LaTeX.

Thessaloniki, GR

• Grade: GPA 7.02/10 (top 30% of class).
• Activities and societies: IEEE.