Part of the Applied sciences team at Microsoft Bing & SwiftKey Mobile experiences

Part of the Applied sciences team at Microsoft Bing

• Part of the Applied sciences team at Microsoft Translate at Azure Cognitive services:– Worked on enhancing low-resource languages and had one model that outperformed our best competitors in the market– Fixed customer-facing bug in model hallucinations that saved our deal with a customer that has 100M+ users– Worked on and shipped around 10+ MT models to production

Working from Egypt's Office in the field of machine learning engines, my main workflow is constructing intelligent systems tackling NLP problems (Text Summarization, Topic Modelling, Recommendation systems.. etc.) also work on main engines that help to retrieve the most relevant articles and topics to the user using many state-of-the-art algorithms with some of the benchmarks. Main Work:- Conduct Natural language processing research- Data analytics and data-driven learning- Engines Dockerization- Ruby on Rails web development- Building endpoint using Falcon/Flask/RoR- Database managing/querying for MongoDB & Neo4J- Building Kafka Streaming pipeline for our NLP Engines

I was working on machine learning engines mostly with text data to analyze and extract information from it. Worked on summarization task to extract to different summaries of an article attractive and extractive summaries.Deployed those engines with Falcon/Flask APIs as ready-for-production endpoint.

Working remotely with Data Systems group in Univeristy of Tartu in Estonia in research projects in Machine Learning and Big data systems.