I'm a Senior Machine Learning Engineer at Schneider Electric, where I lead and contribute to a wide range of AI initiatives across domains. My work includes building scalable deep learning models for export control product classification, developing franchise architectures for model reuse and retraining, and designing robust MLOps pipelines for efficient deployment and inference at scale. I’ve also led the early reinforcement learning infrastructure for internal recommendation systems, and architected sandboxed ontology engines for taxonomy and risk incident control. Currently, I’m leading the technical development of an enterprise-wide Anomaly Detection as a Service (ADaaS) platform. This involves close collaboration with Finance, Compliance, and Supply Chain teams to detect fraud, anomalies, and outliers—while driving actionable mitigation strategies. The platform integrates hybrid rule-based and ML models, with reinforcement learning from human feedback (RLHF), and a robust orchestration layer to support multimodal data. I specialize in bridging the gap between systems engineering and machine learning, with a strong focus on scalable MLOps, model franchise design, and translating cutting-edge research into practical, production-ready solutions. My broader interests include evidential deep learning, interpretability, and the societal impacts of AI, particularly around the future of work, decision-making dynamics, and power concentration in AI systems. My recent work explores model bias and the emerging concept of “You” bias in generative AI personalization.