Fady is a Software Engineer with expertise in Python, Ruby, and related frameworks and has worked with a wide range of technologies. He is skilled in web development, systems engineering, architecture, analysis, and soft skills. He has hands-on experience building and managing apps for various projects and product domains, with a current concentration on Python/Django.
Maintained and added new feature to a US regulated bitcoin options exchange application dedicated to helping individual traders and institutions do more with bitcoin.
Managed to migrate the legacy API to Django. Maintained Core Exchange using Python and C++.
Refactored and migrated old customer facing API services to use Python/Django. Participated in the on-call procedure for monitoring and troubleshooting serious system issues.
Worked on developing an ecommerce consumer platform for bulk grocery and home staples powered by next generation artificial intelligence technology.
Led the search engine team and used Ruby on Rails to deliver various parts of the core application. Developed a Slack bot in Ruby for automating internal work and to help debug issues.
Developed our own analyzer plugin by using Elastic Search extensively. Migrated the core app from MRI Ruby to JRuby for better performance and scalability.
This library is the product of over one year of running our own Akka cluster (orkestra) in production. It started as a routing and registering library for actors and management of routers in the cluster but then it became a comprehensive tool internally for bootstrapping a new service with the least coding effort possible.
This is a real-time ad server that links publishers and advertisers. It was created using Akka, Scala, and Akka-remote. During the project's early stages, I was the only employee of the company and was in charge of creating and managing each individual service in the cluster.
Developed and maintained a search engine for researchers using Node.js, MongoDB, and Elasticsearch; the engine would suggest research papers, important authors, jobs, etc. in a certain scientific topic.
The application also featured a social component where researchers may work together on a particular study.
Created a recommendation engine utilizing Python, Flask, and some fundamental machine learning methods to suggest meals and workouts to customers to help them attain their weight goal (word embeddings).