Cloud adoption among enterprises continues its strong momentum and many enterprises depend on the scale of the public cloud for their digital transformation journey. These enterprises have adopted a development framework and workloads that utilize public cloud scale and an expansive set of services. The developers utilize as well as innovate cloud services such as databases, analytics, IoT, and artificial intelligence/machine learning (AI/ML) that are available to them with the rich set of cloud APIs. This affords the enterprises the ability to innovate faster without the undifferentiated heavy lifting on managing the underlying infrastructure. Cloud edge computing allows workloads to benefit from such cloud innovation and provides a consistent experience to enterprise workloads that require on-prem deployment. These applications usually have low latency requirements for interaction between its components as well as handling massive amounts of data processing and storage. For telco use cases, private 5G (P5G) is an interesting use case for cloud edge computing due to its distributed architecture with a disaggregated set of telco network functions and applications that need to run on-premises due to latency constraints (e.g., gNB and UPF), local data storage (e.g., UDM), or local data processing needs such as applications that require AI/ML capabilities. In this paper, we provide an overview of P5G and relevant hybrid cloud architectures. We review Amazon Web Services (AWS) edge offerings relevant to the P5G space. We then dive into Charter labs undertaking of building an industrial worker safety application that utilizes a hybrid cloud deployment of P5G. The paper describes the architectural choices for such hybrid cloud deployment as well as the use of cloud AI/ML tools as part of the application development and delivery.