More than ever, Machine Learning (ML) as a subfield of Artificial Intelligence (AI) is on the rise and is finding its way into safety-critical software applications. However, when it comes to quality assurance (QA) and trustworthiness, integrating ML models into software comes with challenges that may not be apparent at first glance. The European Union (EU) aims to tackle this problem with new regulatory requirements in the form of harmonized rules on AI (AI Act). It is a risk-based approach with extensive requirements for high-risk systems as well as for foundation models that can be used in various downstream AI systems. Reliable software engineering processes in the form of ML-enabled automated pipelines are likely to become a discerning factor for legally compliant ML systems. Our research project aims to contribute to the field by establishing a theoretical foundation on how to achieve trustworthy AI Act compliant ML systems. Both a literature review and an interview study are ongoing. At a later stage, concrete tools shall be developed, ideally in cooperation with an industry partner, possibly by utilizing the concept of regulatory sandboxes.