Quality Estimation (QE) is essential for assessing machine translation quality in reference-less settings, particularly for domain-specific and low-resource language scenarios. In this paper, we investigate sentence-level QE for English to Indic machine translation across four domains (Healthcare, Legal, Tourism, and General) and five language pairs. We systematically compare zero-shot, few-shot, and guideline-anchored prompting across selected closed-weight and open-weight LLMs. Findings indicate that while closed-weight models achieve strong performance via prompting alone, prompt-only approaches remain fragile for open-weight models, especially in high-risk domains. To address this, we adopt ALOPE, a framework for LLM-based QE which uses Low-Rank Adaptation with regression heads attached to selected intermediate Transformer layers. We also extend ALOPE with the recently proposed Low-Rank Multiplicative Adaptation (LoRMA) for this work. Our results show that intermediate-layer adaptation consistently improves QE performance, with gains in semantically complex domains, indicating a way ahead for robust QE in practical scenarios. We release code and domain-specific QE datasets publicly for further research.