Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, making accurate user intent inference and relevant job retrieval particularly challenging. We present dynamic facet suggestion (DFS), an interactive query-refinement mechanism that facilitates intent disambiguation by surfacing personalized semantic attributes conditioned on the joint user-query context in real time. We propose a policy-grounded, retrieval-augmented ranking framework for facet suggestion, comprising offline taxonomy curation, embedding-based retrieval of top-K candidates, and a distilled small language model (SLM) based candidate scoring. The system is optimized for real-time serving via point-wise single-token scoring and batching/prefix caching. Offline evaluation demonstrates high precision for generated suggestions, and online A/B tests show significant lifts in suggestion engagement and job search outcomes.