Named entity recognition(NER) is a key task in NLP pipeline useful for various applications such as search engines, question answering systems, sentiment analysis in domains ranging from travel, bio-medical text, newswire text, financial text etc. NER is effectively solved using sequence labeling approaches like HMM and CRF. Though, CRF (being discriminative) shows better performance compared to HMM, it uses discrete features and do not naturally capture semantic features. LSTM based RNNs can address this through their ability to deal with continuous valued features such as Word2Vec, Glove, etc. Another advantage of using LSTM lies in its ability to capture the long and short range dependencies through its novel gating structure. This work presents the deep learning based NER using special type of Recurrent Neural Network(RNN) called Bi-directional Long Short-Term Memory(Bi-LSTM). We use a two stage LSTM based network, one acting at character level capturing the n-gram patterns related to NER. Such features are crucial in NER for Indian languages as suffixes used in Indian languages often carry syntactic information. The character based emebeddings, word2vec embeddings and sequence based bi-LSTM embeddings together carry all the requisite features necessary for the NER prediction problem. We present the experimental results on two test datasets from each Indian language such as hindi, kannada, malayalam, tamil and telugu. The accuracies on test-1 datasets of hindi, kannada, malayalam, tamil and telugu languages are 97.82%, 97.04%, 97.46% 97.41% and 97.54% respectively. These are highest accuracy results given by this model when compared with all other models presented by competitors in this shared task [2]. The accuracies on test-2 datasets of hindi, kannada, malayalam, tamil and telugu languages are 97.82%, 96.79%, 96.58% 96.18% and 97.68% respectively. On test-2 dataset this model stood in first position for hindi language and second position for the remaining four languages. The shared task organizers released F-Scores for test-2 datasets of all languages. This model got 94.0%, 84.55%, 84.78%, 89.55% and 91.44% F-Scores on hindi, kannada, malayalam, tamil and telugu languages respectively. All these F-Scores are in second position compared with other models. In overall average accuracy and F-Score of this model on all these five Indian languages is 97.01% and 86.99% which are in second position.