Large Language Models (LLMs) have shown exceptional abilities in comprehending and producing text that closely resembles human language, establishing them as valuable resources for a range of applications. This paper examines the use of LLMs as re-rankers in sequential recommendation systems. We specifically examine how the integration of LLMs into the recommendation pipeline can improve and elevate the quality of recommendations produced by conventional sequential models. The proposed framework utilizes outputs from sequential recommendation models in conjunction with user interaction histories as inputs to the LLM, which re-ranks the candidate items to generate the final top-k recommendations. To assess the effectiveness of this approach, we conduct experiments with six distinct LLMs using two datasets: MovieLens 100K and MovieLens 1M. Our findings indicate that utilizing LLMs as re-rankers enhances the essential recommendation metrics, highlighting their ability to enhance sequential recommendation systems by providing more personalized and precise recommendations to the user.