Digital twin simulation models evolve and are redeployed like software. Engines based on the Discrete Event System Specification (DEVS) have a sound formal basis but offer little support for versioning, automated validation, or continuous delivery in cloud-native environments. This paper proposes a model-centric DevOps architecture that deploys DEVS-based digital twin simulations as managed services. Models are first-class DevOps artefacts written in a declarative language based on YAML (YAML Ain't Markup Language) with a tuple-level mapping to the multiPDEVS formalism. Repository continuous integration (CI) validates publishing manifests, the engine loader performs syntactic, structural, and static semantic checks before every simulation, and published model artefacts are versioned and immutable, so that an earlier validated version can be selected by pinning its identifier. The platform runs as containerised microservices on Kubernetes with engine adaptations that externalise inputs and results and expose lifecycle control. An initial case study on bus route 22 in Riga exercises the lifecycle on an offline model with roughly 115,000 DEVS atomic components and reports single-container throughput and concurrent job execution on one Kubernetes node. Behavioural validation and multi-node evaluation remain future work.

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