Henning Gruhl

Led the development of a Drift Monitoring Tool for ML models and datasets with automated testing and real-time dashboards. Contributed to training and industrialization of a data leakage protection classification model to enhance security compliance. Developed a Git Monitoring Tool using asyncio and aiohttp improving data extraction speed by 80%.

Led end-to-end MLOps development including orchestrating model training and deployment using Databricks, CICD, and Azure ML. Responsibilities included drift and bias monitoring, batch and online inference, and managing cloud infrastructure and security on Azure. Architected and maintained modular Python AI engineering frameworks to standardize workflows and support data scientists.

Led end-to-end MLOps development including orchestration of model training and deployment using Databricks, CICD, and Azure ML, with drift and bias monitoring and batch and online inference. Architected and maintained modular Python libraries to standardize AI workflows, facilitating development and promoting best practices. Managed Azure cloud infrastructure, including Databricks clusters, Azure ML compute, Data Factory & Linked Services, Key Vault secrets, and enabled CI/CD pipelines.