Daniel Alfasi is an applied AI researcher and seasoned software engineer with a decade of experience building cloud-native, security-focused systems and production-ready ML prototypes. He combines deep expertise in representation learning, knowledge graphs, and LLM applications with hands-on architecture and implementation skills across AWS and GCP, enabling rapid POCs that scale into products. Past roles span serverless, high-throughput security pipelines and fraud-detection research, where he led data-centric AI efforts and developed anomaly-detection models. Based in Tel Aviv, he blends rigorous academic training (MSc, Computer Science) with practical engineering across microservices, Kubernetes, and secure cloud patterns—often bridging research and product teams to move ideas into deployment. An under-the-radar strength is his track record of translating exploratory POCs into production features in regulated security contexts.
10 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computer Science, 93.39, Master of Science - MS, Computer Science, 93.39 at Reichman University (IDC Herzliya)
Bachelor of Science (BSc), Computer Science, 90, Bachelor of Science (BSc), Computer Science, 90 at Ariel University
Contributions:56 commits, 55 pushes, 2 comments in 9 months
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