Nasr Ladib

• Participate with Presale Managers for a demo to the clients. • Innovate and Integrate new technologies to increase the production process. • Designed and implemented a git-flow strategy and incorporated it in the continuous integration pipelines. • Identify and implement the most optimal cloud architecture AWS solution for the company software. • Work with the development and the tester team to identify issues, troubleshoot, and make suggestions to resolve applications integration issues. • Dockerizing applications by converting an application to run within a Docker container. • Responsible to write Docker files and Docker compose files to build docker images and containers. • Responsible for setting up Bamboo as our continuous integration (CI) server that can be used to automate the release management for our software application, and creating a continuous delivery pipeline. • Implement the CI/CD using GitLab and GitLab-runner. • Generating code coverage reports from the SonarQube. • Create quality gates in the SonarQube dashboard and enforced the pipelines to fail the builds when conditions were not met. • Integrate the automation test. • Create Ansible playbooks, inventory, and roles to automate repetitive tasks. • Work with Ansible and Ansible Tower, which provides an easy-to-use dashboard, and role-based access control, as a Configuration Management tool, to automate backups (databases, repositories) and synchronized with our backup server. • Create scripts with shell for deployment. • Using Terraform to create infrastructure on the AWS Cloud. • Configuration of server with the required services ( web server Apache, Nginx SSH, SFTP, ...) • Responsible to manage Databases and creating automatic backups.

In order to improve the annotation pipeline of unstructured data, mainly audio, YaiGlobal has added an Audio Segmentation and Classification module using artificial intelligence: • Break down the audio into homogeneous parts that are tagged No-speech, Speech, music, noise. • Develop an AI system using Python and its AI libraries. The challenge was to transform analog audio data into digital data that could be interpreted by the machine. • Implemented scripts using Python, built an AI model and exposed the results via REST-API with the flask framework.