Paolo Tomeo
AI & Data Consultant Agentic Engineer
Bari, Apulia, Italy
Summary
👤
Senior🎓
Top SchoolPaolo Tomeo is an AI & Data Consultant and Agentic Engineer with 11 years of experience building production-ready data platforms, personalization models, and GenAI solutions. He blends rigorous engineering principles from roles at Treatwell, Generali Italia and Agile Lab with research-rooted expertise from a PhD focused on hybrid recommender systems and cold-start problems. Paolo has led cross-functional teams to operationalize ML and agentic workflows, translating experimental research into antifragile, scalable systems. Based in Bari, Italy, he now consults freelance on R&D and long-term collaborations, favoring projects that combine principled engineering with emergent agentic capabilities. A not-often-stated strength is his academic grounding in recommender diversity and graph-based algorithms, which gives him a distinct edge when designing robust personalization and cold-start strategies.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Software Engineering, Doctor of Philosophy - PhD, Computer Software Engineering at Politecnico di BariMaster's degree, Computer Science, Master's degree, Computer Science at University of Bari, Italy
English, Italian, Spanish
Github Skills (10)
dbpedia10
exploit9
algorithms8
recommender-system8
recommendation-engine7
java6
knowledge-graph1
similarity1
semantic1
semantic-similarity1
Programming languages (1)
Github contributions (5)
sisinflab/lodreclib
Mar 2015 - Jun 2016
lodreclib is a Java library to build recommendation engines which exploit the information encoded in Linked (Open) Data datasets.
Contributions:30 commits, 26 pushes, 2 branches in 1 year 3 months
exploitjavarecommendation-engine
sisinflab/LODrecsys-datasets
May 2016 - Oct 2017
Here, we provide mappings to DBpedia resources of items in well known datasets to evaluate recommender systems. This can allows practitioners in the field to evaluate and compare their algorithms with existing approaches.
Contributions:24 commits, 22 pushes, 1 comment in 1 year 5 months
dbpediarecommender-system
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