Ayoub Bendassi

•Engineered backend services enabling integration of machine learning features into production-grade applications.
•Designed RESTful APIs supporting real-time AI inference services.
•Built distributed processing pipelines handling large datasets for intelligent application features.
•Improved system scalability and performance through optimized database queries and asynchronous task execution.
•Contributed to containerized deployment workflows using modern DevOps practices.
•Mentored junior engineers and supported technical planning across cross-functional teams.

•Developed and maintained backend services supporting data-driven applications and internal AI tools
•Designed RESTful APIs for data ingestion, processing, and retrieval systems
•Worked with SQL/​NoSQL databases to manage structured datasets and support annotation workflows
•Implemented data processing scripts to automate dataset preparation and reduce manual workload
•Participated in system optimization to improve performance and scalability of data-heavy applications
•Collaborated with cross-functional teams to integrate software systems with machine learning pipelines

•Designed and developed scalable backend architectures supporting AI-driven applications and machine learning workflows using Python and modern service-based design.
•Built high-performance data processing pipelines for preparing and transforming large-scale datasets used in model training and inference.
•Integrated machine learning models into production environments through robust APIs and automated deployment workflows.
•Optimized system performance and reliability for AI-powered platforms handling large volumes of structured and unstructured data.
•Collaborated with data scientists and ML engineers to support the full end-to-end model lifecycle, from experimentation to deployment.
•Developed reusable internal tools improving dataset quality validation, preprocessing efficiency, and pipeline monitoring.
•Implemented automation solutions reducing manual intervention in training data workflows and improving delivery speed.
•Participated in architecture decisions for scalable infrastructure supporting intelligent services and analytics platforms.