Defined system requirements, success criteria, and acceptance criteria for AI and analytics initiatives across hardware, embedded software, and cloud layers.
Designed and executed a full Measurement System Analysis from scratch — no prior reference existed — establishing a statistically valid human-level performance benchmark as the acceptance criterion for an AI vision inspection model
Validated ML model outcomes against defined acceptance criteria in live production environments, closing the loop from shop-floor sensor to cloud and back
Led hardware-software integration validation across OT/IT boundaries — IoT sensors, edge computing, MQTT, Azure IoT Hub, and containerised deployments
Established cross-disciplinary technical reviews to surface integration risks early and reduce downstream quality failures
Maintained structured delivery documentation and traceability across pilot, validation, and production deployment phases
Communicated quality findings and risk assessments to architects, product owners, cybersecurity stakeholders, and production operations

Delivered Smart Factory integration projects from concept to production, coordinating software, electrical, mechanical, and operations teams
Validated technical delivery against shop-floor realities and defined business acceptance criteria at each project milestone
Contributed to cybersecurity risk assessments and compliance processes for connected industrial systems