AI Quality Engineering Lead – Machine Learning Systeme

AI Quality Engineering Lead – Machine Learning Systeme Job Description • We are looking for an experienced AI Quality Engineering Lead who is responsible for quality assurance of complex corporate platforms with data-driven and AI-supported components. • This position requires a rare combination of in-depth software testing expertise and practical experience in validating AI/ML systems. Main Tasks Test Management & Quality Leadership • Responsibility for end-to-end test management (functional, integration, regression, SIT, UAT) • Definition and maintenance of test strategies, test plans, and quality guidelines • Conducting release approval assessments (Go/No-Go) • Leadership and coaching of QA teams Platform, API, and Data Validation • Testing of APIs, backend systems, and data platforms • Ensuring data quality (completeness, accuracy, consistency) • Validation of data integrity and data flows across systems Test Automation & CI/CD • Promoting an automation approach (Automation-first) • Integration of automated tests into CI/CD pipelines • Analysis of test metrics to improve quality and stability AI / Machine Learning Quality Assurance (Specialized Area) • Validation of machine learning models, in particular: bias detection, drift analysis, explainability (Explainability) • Testing of AI and LLM-based functions • Validation of AI pipelines (MLOps) • Application of data validation frameworks • Support for regulatory and ethical AI requirements (high relevance in Switzerland) Collaboration, Governance & Risk • Close collaboration with Product Owners, Architects, and Developers • Conducting error analyses and risk-based decisions • Creation of audit-compliant documentation Requirements • At least 15 years of experience in software testing / quality assurance • Experience in test strategy, test management, and release governance • Experience in validating AI/ML systems • Knowledge of: Python, TensorFlow / PyTorch, data validation • Experience with: API and backend systems, data-centered architectures • Experience in agile projects, leadership experience in the QA environment