Özge Mutlu
Software Developer / Mathematics Engineer
An excellent problem-solver, able to quickly grasp complex systems and identify oppurtunities for improvements and resolution of critical issues. I am an supportive enthusiastic team member and always excited about new challenges.
Education
Experience
Software Developer
Crescendo Technology Ltd
Jan 2022 - Present
· 2 years and 4 months
Toronto, Canada
Software Developer
Idea Teknoloji
May 2020 - Sep 2020
· 4 months
Istanbul, Turkey
- Extended existing Electronic invoicing software solutions and infrastructure
- Implemented rabbitMQ applications for SMS notifications
- Working closely with the quality assurance teams and designers
- C#
- MsSQL
- RabbitMQ
- Windows Services
Software Developer
Sovos/Foriba
Oct 2018 - Sep 2019
· 11 months
Istanbul, Turkey
- Extended existing electronic invoicing software solutions and infrastructure.
- ASP.NET
- C#
- Entity Framework
- Git
- Kanban
- MVC
- NUnit
- Oracle
- Redis
- TDD
- Web API
- WebService
Full-stack Developer
Sigortam.net
Oct 2016 - Feb 2018
· a year and 4 months
Istanbul, Turkey
- Modified existing project to fix errors and improve performance.
- Developed application and reporting pages for a new campaigns.
- Wrote maintainable and extensible code in a team enviroment.
- Improved security of the payment system.
- ASP.NET
- C#
- Oracle
- Scrum
- Webforms
Projects
Football Network
Sep 2019
- Football Network is a social football discussion mobile application that i've developed its backend services as a freelance project.
- .NET Core
- Dependency Injection
- EF Core
- JMeter
- MsSql
- N Tier Architecture
Graduation Project
Jan 2018
- The purpose of this project is classification of emotions in speech using artificial neural networks. Surrey Audio-Visual Expressed Emotion (SAVEE) Database is used for training and testing. Features which are commonly used in audio recognition studies are prefered in this study. Feature vectors of each sound are extracted from audio database and saved into a JSON file. Part of the data preserved for testing. The network is trained with the rest of the data. Results show that artificial neural networks yields an average accuracy of %76 in the classification of 7 emotion