Edge computing is emerging as a key enabler of low-latency, high-efficiency processing for the Internet of Things (IoT) and other real-time applications. To support these demands, containerization has gained traction in edge computing due to its lightweight virtualization and efficient resource management. However, there is currently no established framework to leverage both containers and unikernels on edge devices for optimized IoT deployments. This paper proposes a hybrid edge system design that leverages container and unikernel technologies to optimize resource utilization based on application complexity. Containers are employed for resource-intensive applications, e.g., computer vision, providing faster processing, flexibility, and ease of deployment. In contrast, unikernels are used for lightweight applications, offering enhanced resource performance with minimal overhead. Our system design also incorporates container orchestration to efficiently manage multiple instances across the edge efficiently, ensuring scalability and reliability. We demonstrate our hybrid approach's performance and efficiency advantages through real-world computer vision and data science applications on ARM-powered edge device. Our results demonstrate that this hybrid approach improves resource utilization and reduces latency compared to traditional virtualized solutions. This work provides insights into optimizing edge infrastructures, enabling more efficient and specialized deployment strategies for diverse application workloads.