This paper describes the Part of Speech (POS) tagger for Bengali Language. Here, POS tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing (NLP) applications, POS tagging is considered as the one of the basic necessary tools. Identifying the ambiguities in language lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. Different methods of automating the process have been developed and employed for Bengali. In this paper, we report about our work on building POS tagger for Bengali using the Deep Learning. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. It is observed from the experiments based on Linguistic Data Consortium (LDC) catalog number LDC2010T16 and ISBN 1-58563-561-8 corpus that 93.33% accuracy is obtained for Bengali POS tagger using the Deep Learning.