Quality Engineer (Digital & AI)

This position reports to the Manufacturing Quality Manager and will be trained to focus on AI and Digitalization, not only working on quality improvement but also in bridging quality management, data analytics, and AI-driven solutions to support manufacturing excellence. You will be part of a dynamic, cross‑functional team with hands-on experience working on innovative projects that drive continuous improvement of quality processes, operational performance, and digital transformation across the production environment.In this role, you will learn to apply quality engineering principles together with AI, machine learning, and data analytics to monitor, analyze, and improve quality KPIs, identify process deviations and root causes, and enable predictive and prescriptive quality management. You will have the exposure to support the evolution from traditional quality control to digital and intelligent quality systems, and trained to help the organization in reducing defects, scrap, and rework while improving yield, reliability, and customer satisfaction.Learning Outcomes:• Plant Data Analytics with AI: Apply AI and data analytics to support manufacturing quality management and operational excellence, enabling data‑driven decision making across production lines.• Descriptive Analytics: Visualize current operations and interpret historical data to provide a clear overview of plant and quality performance. Monitor and analyze quality KPIs (e.g. yield, defect rate, scrap, rework), process and machine data, and energy consumption to identify gaps, risks, and opportunities for continuous improvement.• Diagnostic Analytics: Investigate the root causes of quality and process deviations using data mining, correlation analysis, and statistical or machine‑learning techniques. Exposure to support root cause analysis (RCA) for quality issues, process instability, and abnormal trends in production KPIs.• Predictive Analytics: Develop models to forecast future quality and operational outcomes based on historical and real‑time data. Learn to enable early warning and preventive actions for quality risks, equipment failures, process drift, and material or inventory constraints (e.g. predictive maintenance, yield or defect prediction).• Prescriptive Analytics: Utilize advanced analytics, AI models, and expert knowledge libraries to recommend corrective and preventive actions. Support prescriptive quality management by reducing dependency on manual judgment and experience, and by standardizing decision‑making for operators and engineers.• Process Automation: Design and implement automation tools and AI solutions to improve efficiency and consistency in quality and manufacturing workflows, including: Automated data analysis and reporting, Image and vision‑based analysis for product defect detection and classification, Reduction of manual, repetitive quality and data‑handling tasks.• Digital Transformation: Support the digitalization of quality and manufacturing processes and learn to develop solutions that transition manual or paper‑based workflows into digital, data‑driven, and traceable systems, aligned with Quality Management System (QMS) requirements.• Web Application Development: Redesign and modernize legacy Windows‑based applications into web‑based solutions with intuitive user interfaces, enabling broader accessibility, scalability, and integration with manufacturing and quality data systems.Technical Skills and Competencies:• Educational Background: bachelor’s degree or master’s degree in engineering fields (e.g. Computer Science, Computer Engineering, Data Science, AI/Machine Learning, Information Technology, Mathematics and Statistics, EEE or other relevant fields).• Programming Skills: Proficiency in programming.• Web Design: Experience in web design is an advantage.• Experience in AI (Gen AI, Machine Learning) is an advantage.