I am a young developer & an engineering student, and you are welcome to my web-portfolio.
I currently work as an apprentice DevOps engineer & Scala developer at the Orange company. I have the opportunity to work with DevOps tools such as Jenkins, Travis, Scala SBT, Docker, Ansible, and also to learn how the application performance management works in the Orange's Application Performance Experts team.
I am highly motivated by the Big Data in general, and about technologies such as Machine learning, etc. I am constantly learning (by myself, or at school) about these subjects, and I will go further and study in that field after being graduated at ENSSAT, from sept. 2017. Let me know if you are interested in my profile for a potential internship in this domain!
You will find on this website some information about me, my skills, and about some of my projects.
Paris - France
Guitarist / Singer / Music player & lover
Play badminton twice a week, occasional swimmer, love long hikes and nature activities
mid-2016 - 2017: ERASMUS in Roskilde University, Denmark
2014 - 2017: student at the engineering school ENSSAT Lannion - Computing Science
2012 - 2014: D.U.T GEII Rennes (Technical Degree in Electronics & Computing Sciences)
Fondamentaux pour le Big Data
2016: Japan (15 days), Lisboa (7 days), Ireland (10 days), Denmark (5 months)
2015 (30 days): Scotland, Glasgow
2014 (3 months): Ireland, Athlone AIT during an internship
& many others. . .
English: fluent & good technical skills (B2 level)
Spanish: basic knowledge
Danish: basic knowledge
Functional programming: Scala, 2 years experience, daily use. Currently following the Scala specialisation on Coursera.
Git: Daily use, advanced workflow (branch, checkout, tags, ...).
Object-oriented programming: Java, 3 years experience, several projects.
C: robust experience (loop invariant, strict methodology & concepts, graphs).
Databases: SQL (MySQL, PostgreSQL...) / NoSQL (MongoDB)
Click on a project to learn more!
Clone of Boulder Dash, done in Java, use of MVC pattern.
Use of R to analyse a Dataset concerning the sinking of the Titanic. Machine Learning models used to predict the survivors of the crash.