Ilyas Tajmout

• Designed and automated end-to-end ML pipelines using DVC, MLflow, and GitLab CI, achieving robust version control and traceability for data and models. • Integrated Optuna into training scripts for hyperparameter optimization and performance analysis. • Developed a Flask-based model engine integrated with automated deployment pipelines. • Configured Elasticsearch and Kibana for multi-user remote query access and real-time data visualization.

Key Responsibilities: • Built data acquisition systems in C++ integrated with RabbitMQ for real-time data ingestion. • Developed convolutional neural networks (CNNs) with multi-attention layers for anomaly detection. • Conducted exploratory data analysis using Spark and created validation pipelines in Python and C++. • Optimized and packaged models using TensorRT for inference efficiency. Key Achievements: • Achieved 97% accuracy in anomaly detection for internal testing phases. • Reduced model inference times by 4x through TensorRT optimization. • Implemented a robust data acquisition system enabling real-time model training and evaluation.

Key Responsibilities: Designed and implemented convolutional neural networks (CNNs) for trajectory prediction in large-scale 3D printing applications for the construction industry. Conducted an extensive review of state-of-the-art techniques in additive manufacturing to identify optimal methods for robotic trajectory prediction. Integrated robotic arm systems with AI-driven prediction models, leveraging ROS (Robot Operating System) for communication and control. Developed proof-of-concept solutions for large-scale 3D printing, combining mechanical design and AI models. Key Achievements: Delivered a functional Proof of Concept (POC) for a large-scale 3D printer with trajectory optimization capabilities. Enhanced printing precision and efficiency through predictive modeling, ensuring smoother operation of robotic arms. Established the foundation for AI-driven solutions in additive manufacturing processes for construction.

-Assurer le suivi et bon fonctionnement interne de chaque service. -Supervision des réseaux de distributions électrique HTA/HTB. -Assurer la télécommunication entre la section de dispatching et les postes de distribution. -Accompagner la section ligne dans la détection et correction des défauts de lignes. -Assurer le bon fonctionnement des indicateurs de passage de courant de défauts.

- Prise en charge de la phase de design conceptuel. - Analyse des performances des différents profils d’aile. - Analyse de marché des UAV déjà existant. - Dimensionnement des paramètres des ailes. - Calcul des surfaces de control.