Simon Stiebellehner is a Lead MLOps Engineer at Transaction Monitoring Netherlands (TMNL), a worldwide unique initiative of the big 5 banks of the Netherlands that uses advanced analytics to tackle anti money laundering. Working in one of the most strictly regulated data environments in finance, he builds the machine learning platform that keeps model development reproducible, auditable, and compliant with GDPR and fintech security requirements.

His MLOps expertise spans the full platform lifecycle: cloud infrastructure with Kubernetes and Terraform, experiment tracking and model registries, deployment patterns for batch and online serving, and orchestration with tools like Airflow. He approaches machine learning operations as the combination of people, processes, and technology, stitching SaaS and open source components into a coherent platform with strong metadata, lineage, and monitoring practices. A recurring theme in his work is user centric design, making sure notebooks and data science workflows genuinely serve the people who rely on them.

Simon is also a university lecturer for Data Mining and Data Warehousing, bringing production realities into the classroom. Through his teaching and podcast conversations, he shares pragmatic guidance on ML platform design, build versus buy decisions, staffing MLOps teams, and running compliance ready model operations from scratch.