AI-powered candidate matching is becoming one of the most important things in recruitment today because matching job descriptions with candidate profiles is really challenging. Most of the traditional platforms use keyword-based matching which usually fails to understand the real context of skills or job requirements, and because of this a lot of unqualified candidates are suggested while the right ones get ignored which makes hiring inefficient. To solve this, we propose a semantic matching framework which tries to improve matching accuracy by using Sentence-BERT (SBERT) to calculate contextual similarity between job descriptions and candidate profiles by converting the text into dense embeddings and then using cosine similarity to measure compatibility. Also, we apply a TF-IDF based keyword extraction to find missing or underrepresented skills in candidate profiles, which helps in finding skill gaps and giving personalized feedback. Experiments on different job and resume datasets show better precision and clarity than traditional keyword matching. By understanding relationships between professional terms and job roles, the framework connects recruiter expectations with candidate potential. This study also shows that contextual embedding can really improve recruitment intelligence and in future it can include transformer fine-tuning and real-time recommendation features.
