This paper discusses the Retrieval-Augmented Generation (RAG) system submitted to the MTRAG-UN shared task on multi-turn conversational question answering. The paper describes the proposed solution for Task A (Document Retrieval) and Task C (Full RAG Pipeline), focusing on retrieval robustness and grounded response generation in complex English multi-turn dialogs. The proposed retrieval architecture uses a cascaded hybrid pipeline, which combines sparse retrieval (BM25) with dense bi-encoder models (BGE-base-en-v1.5 and E5-base), integrated via Reciprocal Rank Fusion and refined using a weighted ensemble of cross-encoders. For the generation part, the top-3 retrieved passages are injected into FLAN-T5-Large using an anchor-prompting strategy to output grounded faithful responses. Experimental results show that the proposed hybrid retrieval framework with multi-stage reranking significantly enhances passage selection, particularly for non-standalone conversational queries. Further analysis reveals persistent difficulties in handling underspecified and unanswerable questions, as well as an increased susceptibility to retrieval noise in later dialog turns.