Qonto is a European neobank for professionals. To improve a higher quality of service for its clients, in 2018 it developed its own “Core Banking System”, meaning that it maintains itself all its clients' accounts and process all their transactions. (Beforehand it was relying on an external partner to do so.) At Qonto, I was part of the Ledger team, which maintains the “source of truth” for all accounts and their transactions. These micro-services were implemented using a Go plus PostgreSQL stack. Moreover, I did multiple interventions to improve and maintain Qonto’s billing system which is implemented using Ruby on Rails.

Qonto uses an advanced micro-service architecture with over 80 services (and counting!) being continuously deployed relying on Gitlab, Kubernetes and Argo CD.

PacketAI aims to develop an IT infrastructure monitoring platform, similar to Datadog and Dynatrace, but equipped with Machine Learning to predict incidents in advance and locate their root cause.

I started when PacketAI had just received its seed funding, with only two other developers. I was able to quickly get a grasp on their stack, and within days of my arrival, I started adding new features to the agent, a software running on the client hosts collecting events and metrics. I designed and developed from scratch all PacketAI microservices, all in Go, plus a Logstash node.

• PacketAI product is based on the ELK stack: The Beats to produce data and Logstash to transform and forward it to ElasticSearch.
• Data is streamed using Kafka pipes.
• Communication between microservices using REST APIs.
• Many tools or test environments are deployed with docker-compose. I was involved in the development of the CI/CD pipelines of our Go projects on GitLab.
• Scrum method used based on Trello and GitLab.
• Mentored the integration of an intern to the team.

CSRC is a publicly-funded research center within KAIST university. I was free to define the problems I worked on, and figure potential solutions, then develop and design their implementation, and finally test and evaluate these prototypes. This experience allowed me to demonstrate my abstraction ability: find solutions based on principles.

I also made full use of my engineering mind: I completed three large projects. First, a modification of the code of Linux Kernel memory allocation for Drivers (in C). Second, an improvement of the dynamic testing tool of LLVM, a compiler infrastructure project written in C++. This project was merged into the mainline by a team at Google. And lastly, Ankou, my largest project, is a fuzzer I developed from scratch in Go. Ankou found more than a thousand unique crashes in open source projects.

• At CSRC collaboration was done using Slack and Gitlab. Most notably our survey involved seven members. All contributions made via merge requests.
• Experiments setup in Docker containers to be reproducible and scalable to multiple servers. Command-line tools are invaluable: htop, grep, find, etc…
• Ankou (described below), I started as an investigation on the usage of machine learning techniques to improve fuzzers bug finding ability. For this, standard python libraries were used: Keras, TensorFlow, Numpy, Pandas. The two parts of the project, in Go and Python, were communicating via RabbitMQ.