Shortly about deep learning (for reference):
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.
Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.
You should think of them as different approaches. A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models.
The primary connection between the two is dropout, a particular method of training deep neural nets that's inspired by ensemble methods.
Deep neural networks could - in principle - be a component of an ensemble of machine learning algorithms, yes. Ensemble method basically just means use multiple algorithms and combining their output somehow.
Other than that, I don't see any special connection between deep learning and the idea of ensemble methods. DL is just one more tool in the toolkit.