Samy Ben Hassan

I am currently a DevOps/Data Engineer on the Renault EASY CONNECT project, aiming to develop a connected platform for

Renault vehicles, improving service management and user experience. In this role, I have:

• Deployed and orchestrated containerized applications with Docker and Kubernetes on Amazon EKS, ensuring scaling

automatic and continuous availability.

• Automated infrastructure provisioning on AWS using Terraform and Ansible, ensuring consistent deployments

• Implemented Apache Spark and Kafka for real-time data processing, enabling faster and more reliable analysis with

adapted Python scripts.

• Orchestrated data workflows with Apache Airflow, optimizing the management and execution of data processing tasks.

I worked as a Data/DevOps engineer on the Datavenue project, a key Orange initiative aimed at harnessing the potential of data and the Internet of Things (IoT) to deliver innovative solutions and improve decision-making. In this context, I:

• Optimized Apache Spark jobs for data processing, reducing the processing time of complex analysis tasks.

• Implemented Apache Kafka for messaging and log aggregation, ensuring smooth communication between services.

• Designed, developed and maintained data flows with Apache Airflow, enabling complete automation of ETL pipelines.

• Automated the provisioning and configuration of data infrastructure on AWS with Kubernetes using Terraform and Ansible. Skills: Git Linux Python AWS Apache Spark Apache Kafka Apache Airflow PostgreSQL Docker Kubernetes

I worked as a Machine Learning/DevOps engineer on the DentalAI project, aiming to help dentists provide optimal treatment recommendations to patients for dental improvements. In this context, I:

• Developed and trained machine learning models from scratch in Python, using TensorFlow, PyTorch and scikit-learn, based on recent research papers, and containerized these models with Docker for easy deployment on AWS.

• Implemented data pipelines with Apache Airflow, integrating Apache Spark for mass data processing and Apache Kafka for real-time data flow management, improving the accuracy and efficiency of machine learning models.

• Deployed the models on AWS using Terraform for infrastructure as code management and Ansible for configuration automation, and orchestrated deployments with Kubernetes to ensure optimal scaling and management.