Riccardo Murgia

Developed and maintained Big Data processing pipelines for vehicle telemetry data (CAN bus / ECU), ensuring scalable data ingestion and processing. Engineered contextual statistical features (environmental and usage-based) to support analytics on real-world vehicle usage patterns. Designed and implemented unsupervised clustering models to segment driver behavior, generating insights that supported improvements in vehicle quality and performance. Collaborated with cross-functional teams to translate business needs into data-driven solutions and delivered dashboards and reports to stakeholders.

During my internship, I worked as an AI Engineer, focusing on the design and implementation of a Multi-Agent System powered by Large Language Models like GPT and frameworks such as LangChain. The primary goal was to simplify data analysis workflows—from extraction and processing to visualization—by enabling conversational interfaces for non-technical staff. I also designed scalable database schemas and automated data ingestion pipelines to streamline data collection and preparation from diverse sources.

Virtual assistant powered by LLMs (GPT), structured as a Multi-Agent System using LangChain, to automate the full data analysis workflow, enabling non-technical staff to query, process, and visualize data conversationally. Designed scalable data ingestion pipeline from heterogeneous sources and implemented a scalable database schema and data governance mechanisms to support analytics.