Design optimization of gas turbine blades is a complex multidisciplinary task requiring computationally expensive physics simulations. To perform them, a multitude of computer-aided engineering tools are used, often with machine-learning surrogates for rapid prototyping, all integrated into the optimization cycle. However, current approaches to such integration are hindered by the need for labor-intensive manual setups, vendor lock-in and a lack of scalable, automated workflows. We present a novel cloud-based architecture for building flexible optimization pipelines using containerized components. The proposed solution employs serverless containers, asynchronous messaging and cloud services to ensure the system’s scalability, portability and resilience. Additionally, it follows MLOps principles to achieve reproducibility and efficient lifecycle management of machine learning models used in the optimization process. Unlike existing frameworks, our solution minimizes user setup complexity, allows easy integration of various software into the optimization cycle, and avoids vendor lock-in through open-source technologies and standard cloud APIs. Experiments with aerodynamic design optimization of gas turbine blades demonstrate the system’s scalability, fault tolerance and successful integration of surrogate models for rapid blades prototyping. Furthermore, the system’s flexibility and extensible architecture make it applicable to a broader range of engineering design optimization tasks beyond gas turbine blade aerodynamics.