Bio

A Machine Learning Engineer driven by problem-solving and with a proven academic record in the field of Artificial Intelligence. A competent programmer exposed to both dynamically (Python) and statically typed (Java) programming languages, with commercial experience in the development of big-data pipelines using open-source cluster computing frameworks (Apache Spark, Flink, DataFlow). A certified Apache Spark and Neo4j developer, Google Cloud Engineer and AWS ML Engineer. A Graph-Analytics practitioner with hands-on experience in handling and statistically analysing large graphs and designing genetic algorithms applied on graphs (Spark GraphX, Flink Gelly). My years of teaching in Higher Education and presenting in high-calibre conferences (both in academia as well as in the industry) enabled me to hone my presentation and communication skills, while my exposure to interdisciplinary work through my PhD and my role as a consultant over the last 5 years allow me to quickly adjust to new environments and productively interact with people from different (scientific) backgrounds. When managing projects, I try to approach things from different angles and aim at building consensus around new ideas and filling gaps throughout the development lifecycle. Applying software development best practices (Agile, Scrum, TDD) is life for me.