TalentBridge AI: An Explainable Semantic and Generative Intelligence Framework for Recruitment Optimization and Skill Readiness Assessment

TalentBridge AI: An Explainable Semantic and Generative Intelligence Framework for Recruitment Optimization and Skill Readiness Assessment

Viraj Khanvilkar                                                                                                                                      Jai Punde

Dept. of Computer Engineering                                                                                 Dept. of Computer Engineering,

Atharva College of Engineering                                                                                 Atharva College of Engineering

Mumbai, INDIA                                                                                                                                 Mumbai, INDIA

e-mail: khanvilkarviraj-cmpn@atharvacoe.ac.in                                                       e-mail: jaipunde74@gmail.com

Abhishek Paradkar                                                                                                         Prof. Mahendra Patil

Dept. of Computer Engineering                                                                                 Dept. of Computer Engineering,

Atharva College of Engineering                                                                                 Atharva College of Engineering

Mumbai, INDIA                                                                                                                                  Mumbai, INDIA

e-mail: aparadkar315@gmail.com                                                                     e-mail: mahendrapatil@atharvacoe.ac

Abstract: The exponential digitization of recruitment workflows has introduced unprecedented scale into candidate screening pipelines, rendering manual evaluation both computationally infeasible and operationally inefficient. While Applicant Tracking Systems have become ubiquitous in mitigating screening  overhead, most contemporary implementations rely on lexical keyword matching and Boolean rule

These techniques are inherently brittle to semantic variation, resulting in disproportionately high  false negative rates when syntactic mismatch occurs between candidate resumes and job descriptions despite  substantive alignment in skills and experience. This paper presents TalentBridge AI, a hybrid recruitment intelligence framework that reconceptualizes automated hiring as a semantic similarity estimation and  readiness optimization problem. The proposed architecture integrates transformer based dense vector  embeddings for high resolution semantic alignment with generative artificial intelligence for structured interpretability and skill gap remediation. Candidate resumes and job descriptions are embedded into a shared latent semantic space, enabling continuous similarity estimation via cosine similarity metrics. In parallel, a generative reasoning module constructs dependency aware skill graphs that transform ranking outcomes into personalized upskilling roadmaps. Extensive empirical evaluation conducted in a controlled pilot deployment demonstrates a 91.3 percent accuracy in role classification, a 94 percent structured parsing  success rate, and a 40 percent reduction in end to end screening latency compared to baseline ATS systems.  Beyond ranking candidates, the system operationalizes a reject and upskill paradigm, repositioning recruitment systems from passive exclusionary filters into adaptive human capital optimization platforms.

Keywords: Semantic Retrieval, Transformer Embeddings, Generative Artificial Intelligence, Explainable Recruitment Systems, Skill Dependency Graphs, Human Capital Optimization