Rayene S.

- Missions : • Developed Generative AI agents for enterprise and business use cases • Designed and implemented multi-agent architectures using A2A, ADK, LangChain, and LangGraph • Built RAG pipelines (Retrieval-Augmented Generation) for reasoning over structured and unstructured data • Orchestrated agent workflows including tool calling, memory management, and reasoning chains • Contributed to the deployment and productionization of LLM-powered applications • Performed integration, testing, and optimization of scalable agentic AI systems.

Missions : • Developed a Generative AI companion by combining machine learning and cloud data engineering (Python, SQL, GCP). • Designed prompts and automated queries to enhance data lineage understanding and usability. Résultats : • Improved data exploration and lineage explanation for the Data Europe teams. • Accelerated decision-making and increased operational efficiency.

Missions: • Developed AI algorithms for provision calculations. • Optimised provision challenges using machine learning and deep learning techniques. Results: • Improved calculation accuracy by 80% • Compared to traditional Chain Ladder methods.

Missions: • Automated risk management processes using Python. • Collaborated on compliance initiatives to align risk management with regulations. Results: • Increased efficiency in threshold optimization • Compared to traditional Excel-based processes.

Missions: • Conducted data scraping to build comprehensive datasets. • Created visual representations of data to effectively communicate findings. Results: • Developed actionable insights for the tourism sector.