This paper describes our system for the MT-RAG (Multi-Turn Retrieval-Augmented Generation) shared task, which addresses the challenge of multi-turn conversational question answering using retrieval-augmented generation. We participated in three sub-tasks of Task 8: Task A (retrieval), Task B (generation with reference passages), and Task C (end-to-end RAG). For Task A, we evaluated multiple retrieval approaches including BM25, BGE, and hybrid methods, achieving best performance with ELSER (Elastic Learned Sparse EncodeR) with nDCG@5 of 0.4018 (Rank 24/38). For Task B, we employed the Mistral-7B-Instruct-v0.2 model via HuggingFace for response generation using gold reference passages, achieving a harmonic mean score of 0.6976 (Rank 13/26). For Task C, we combined ELSER retrieval with Mistral-7B generation, using top-5 retrieved passages as context, achieving a score of 0.4289 (Rank 23/29). Our system demonstrates the effectiveness of learned sparse retrieval methods and instruction-tuned models for multi-turn conversational RAG scenarios.