Amine Hannoun

Worked within Schneider Electric's AI Hub under Sylvain Marie, Senior Group Expert in Analytics & AI, contributing to production-level AI systems used at industrial scale.

• Designed and integrated an AI pipeline component handling automated feature transformation, reducing manual preprocessing time by ~60% on internal datasets.

• Built a reusable Python module for probabilistic data analysis, now integrated into the team's internal ML toolkit — improving model accuracy by 12% on anomaly detection tasks.

• Implemented an API-based interface allowing non-technical teams to query ML models directly, removing the need for manual intervention and cutting response time from hours to seconds.

• Collaborated with senior AI engineers on code review, model validation, and deployment readiness — gaining direct exposure to enterprise-grade AI workflows.

Takeaway: learned how to ship AI solutions that are reliable, scalable, and usable by real teams — not just prototypes.

Developed an interactive graphical interface in Python designed to help professors teach Machine Learning concepts effectively. The tool provides step-by-step visual demonstration of the AdaBoost algorithm, showing how weak learners are combined into a strong classifier through boosting iterations.

Key features:

Real-time visualization of AdaBoost training process and decision boundaries

Automated ML pipeline for data preprocessing, model training, and evaluation

Interactive controls to adjust hyperparameters and observe their impact instantly

Clear visual output of each boosting round for educational purposes

Built with Python using libraries such as Scikit-learn, Matplotlib, and Tkinter/PyQt

Worked on securing industrial control systems (OT/ICS) within critical infrastructure environments, applying IEC 62443 standards — while integrating AI-powered tools to automate security workflows.

• Conducted full security audits of industrial systems (OT/ICS), identifying critical vulnerabilities across network architecture, access control, and communication protocols.

• Built an AI-assisted reporting pipeline using LLMs to automatically generate structured audit reports from raw technical findings — reducing report writing time by ~70% and ensuring consistent IEC 62443 compliance formatting.

• Developed a Python automation script that parses security scan outputs and feeds them into an LLM prompt chain, producing actionable remediation recommendations tailored to each client's infrastructure.

• Completed the AIR Cyber Bronze certification (industrial cybersecurity), gaining hands-on knowledge of OT threat modeling and risk assessment frameworks.

• Designed reusable report templates (PDF/DOCX) auto-populated by AI from audit data, enabling the team to deliver client-ready documents within hours instead of days.

Takeaway: bridging AI automation and industrial security — making complex technical audits faster, more consistent, and accessible to non-technical stakeholders.