Naples, Campania, Italy
I lead Product Development for Digital Transformation Solutions & Adoption within the Digital & AI Center of Excellence at Hitachi Rail, with responsibility for defining and scaling AI-driven products across industrial operations. My focus is on turning artificial intelligence and data capabilities into structured, deployable systems that integrate with real-world processes. This includes setting product vision, building multi-year roadmaps, and establishing governance models that enable consistent delivery and adoption across sites and business units. I operate at the intersection of technology, product, and operations—aligning engineering, IT, and business stakeholders around clear use cases and measurable outcomes. My work spans areas such as computer vision for quality inspection, data platforms for operational insights, and AI-supported decision systems. I bring a combination of technical depth in machine learning and a strong product and systems mindset. I focus on building solutions that are robust, maintainable, and scalable, prioritizing integration and long-term value over isolated innovation. My approach is pragmatic: start from the operational problem, design within constraints, and apply the right level of technology to deliver impact. I see AI not as a standalone capability, but as part of a broader system that includes data, processes, and people. I am particularly interested in driving the industrialization of AI—moving from experimentation to repeatable, enterprise-scale deployment.
Leading the strategy, governance and lifecycle management of enterprise digital products across AI, GenAI, agentic automation and Business Intelligence domains. Responsible for defining product direction, portfolio prioritisation, value realization frameworks and operating models that enable scalable, secure and business-aligned digital solutions across global functions and lines of business. Driving the adoption of governed, measurable and production-ready digital capabilities through collaboration with Product Delivery, IT, Data, Architecture, Security and Business stakeholders. Focused on building enterprise product management standards, enabling responsible AI adoption, and ensuring digital investments translate into measurable operational and strategic value.
Leading the Product function within Digital Solutions in the Digital & AI Center of Excellence, defining and driving the product strategy for enterprise-level digital and AI solutions across the business. Accountable for product vision, portfolio governance, and roadmap execution, ensuring scalability, reusability, and alignment with corporate digital transformation goals. Overseeing a portfolio of AI-enabled products and platforms (e.g. AI-driven quality inspection, data platforms, decision intelligence), guiding cross-functional teams across data science, software engineering, R&D, IT, and operations. Acting as a strategic interface between business lines and the CoE, enabling the adoption of AI at scale and maximizing business impact. Product Leadership · AI at Scale · Digital Transformation · Product Portfolio · CoE Governance · Enterprise AI
Responsible for product management of AI & Machine Learning solutions, acting as the bridge between data science, engineering, and business stakeholders. Led the end-to-end product lifecycle from use case ideation and prioritization to PoC, industrialization, and scale-up. Defined product vision, roadmap, and success metrics for AI-driven applications (e.g. computer vision, predictive analytics, decision support systems), ensuring alignment with business objectives and operational constraints. Coordinated cross-functional teams (AI/ML, IT, R&D, Operations, Suppliers) and translated complex technical capabilities into tangible business value. AI Product Strategy · ML Lifecycle · Use Case Prioritization · PoC to Scale · Stakeholder Management · Business Value
Worked on the development and application of machine learning solutions in industrial and manufacturing environments, with a focus on turning raw operational data into actionable insights. Designed and implemented models across multiple use cases, including predictive analytics, anomaly detection, and computer vision, supporting quality improvement and process optimization. Collaborated closely with engineering and operations teams to understand system behavior, data generation mechanisms, and real-world constraints. Contributed to the full lifecycle of ML solutions—from data acquisition and preprocessing to model development, validation, and deployment—ensuring solutions were not only technically sound but also applicable in production contexts. Led research and development activities on welding process data, resulting in a patented machine learning approach for analyzing signals from robotic welding systems to detect patterns, assess quality, and support defect identification. This work combined domain knowledge of industrial processes with advanced data analysis techniques to improve reliability and traceability. Focused on building a strong foundation for scalable AI applications by structuring data pipelines, improving data quality, and aligning model outputs with operational needs.
- Salesforce, Einstein AI & Machine Learning - Microservices and Integrations - Azure DevOps - Data and Context Analysis - Development & Audit of project deliverables - Training Junior colleagues
Accenture Machine Learning Center of Excellence - Machine Comprehension and NLP - Face Recognition and Verification - Computer Vision for Object Recognition - GPU HPC - Data and Context Analysis - Development & Audit of project deliverables - Training Junior colleagues - Microservices development and deployment with Docker
- Social network connections and Network Topology analysis - Deep Learning for Image Processing - Deep Learning for Text Processing
Pattern Recognition & Computer Vision Laboratory