This talk is aimed to discuss the challenges and the research opportunities in Indic Language Computing. The talk begins with an introduction to Large Language Models and the capabilities of these Large Language Models. Specifically, the talk covers auto-regressive language models like GPT2, GPT3, etc. The talk briefly describes the resource requirements for training such large language models. Specifically, we will look at the requirements in terms of training corpora, the model capacity, and the computation budget for training such large language models. Recently, there has been widespread interest in creating better large language models by aligning them with the human intention. These approaches include instruction-finetuning, learning with reinforcement learning with human feedback, etc. The talk covers some of the popular approaches for large language model alignment. The talk later delves into the challenges of creating such large language models for Indic languages. We will first look at the efforts of AI4Bharat in creating large scale monolingual corpus for various Indic languages. We will discuss the potential challenges and research opportunities. We will discuss some of the evaluation benchmarks like IndicNLG and IndicXtreme for Indic languages. We will also discuss the Olive and instruction-following large language model for Odia language. The talk concludes by briefly summarizing the research opportunities in Indic Language NLP.