This chapter presents a novel approach to modeling and simulating chemical processes by integrating neuro-symbolic reasoning, Infrastructure as Code (IaC), and modern cloud-native architectures. We explore how symbolic representations (e.g., XML-based configurations) and neural inference can be combined to model chemical systems intelligently, enabling automatic configuration and adaptation of simulation workflows. The concept of Infrastructure as Code is introduced as a foundation for building modular, scalable, and repeatable chemical simulation environments. This is demonstrated through examples that represent chemical process components using Spring integration flows and cloud services such as AWS Lambda, EC2, and S3. A dedicated section outlines the use of the Enterprise Service Bus (ESB) to orchestrate these modular components. We further examine how modeling tasks can be dynamically executed using serverless infrastructure and how reagent flows can be simulated asynchronously using message queues like AWS Simple Queue Service (SQS). An architectural perspective is provided through a real-world example of simulating ammonium nitrate production, illustrating the full stack of the proposed system. Finally, we take a broader view of process simulation, highlighting how these cloud-native, neuro-symbolic frameworks can accelerate the design, deployment, and optimization of chemical processes in both research and industrial settings.