Leonardo Gutierrez

Supporting the DataLab team in developing AI solutions in the banking sector.

- Contributed to the development of Generative AI (GenAI) applications, utilizing advancements in open-source Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures with private banking data.

- Designed and implemented evaluation and monitoring frameworks for GenAI models to ensure reliability, accuracy, and compliance in production environments.

- Developed and optimized ETL pipelines to streamline transaction data flows, enabling the creation of effective machine-learning models for fraud detection in AML/KYC use cases.

- Led the RD&I workstream to develop cutting-edge solutions in ML monitoring (drift detection) in collaboration with UniLux (SNT) researchers.

- Supervised the development of Recommender Systems to enhance marketing strategies and drive customer engagement.

Conducting applied research in the Data-Driven Engineering team, providing cutting-edge solutions for the automotive industry with applications in virtual tire design and tire intelligence products.

- Responsible for the planning, coordination, execution, deployment, and monitoring of data-driven engineering projects involving a cross-functional global team from the USA and Europe.

- Developing ML models to support advanced tire simulation capabilities leveraging multi-fidelity data.

- Creation of novel algorithms combining multi-performance optimization, FEA simulations, and ML to support the virtual tire design process.

- Developing ETL pipelines to process semi-structured data by ingesting millions of records from multiple sources to the cloud data lake storage within a CI/CD process.

- Exploiting large tire sensor data to build and deploy ML models via REST-API systems.

- Communication of project status, milestones, and challenges to team members, managers, and senior directors.

Performing applied research to develop tools and solutions in the automotive industry, leveraging data mining, machine learning and optimization at Goodyear and LIST partnership.

- Providing input to the definition of research concepts in collaboration with LIST and Goodyear.

- Designing and developing a similarity algorithm to compare high-dimensional tabular data

containing mixed-type (numerical and categorical) variables.

- Developing kernel-based ML algorithms to predict multiple tire-related performances.

- Developing a multi-performance optimization engine leveraging data-driven objectives and

physical constraints.

- Contributing to knowledge generation and dissemination of results as technical reports, research

papers, presentations, and software packages.