Tatapudi Satyanarayana, I. Lakshmi Manikyamba
Traditional Applicant Tracking Systems (ATS) depend on rigid keyword matching, creating significant semantic blind spots, while monolithic cloud-hosted Large Language Model (LLM) applications introduce severe network latency, high operational costs, and vulnerabilities to infrastructure rate limits (429 Exceptions). This paper presents CareerMind, a hybrid, fault-tolerant intelligent framework that shifts candidate evaluation from high-latency cloud architectures toward an optimized, multi-tier localized ecosystem. The framework deploys an Offline Feature Engine Tier using a sub-linear TF-IDF vectorizer coupled with a local multi-class Linear Support Vector Classifier (Linear SVC) matrix. This layer maps unstructured profile tokens across maximum-margin hyperplanes to predict target job domains in under 5 milliseconds with a 92.23% Global Macro F1-Score on standard commodity hardware. Clean professional text blocks are then indexed inside a localized RAM-cached FAISS vector database to compute semantic matches using geometric L2 Euclidean distance and normalized cosine proximity metrics. Finally, the architecture secures system reliability via a Runtime Infrastructure Fault-Tolerance Layer containing an autonomous model fail-over routing algorithm that catches cloud exceptions and hot-swaps active traffic pools to backup engines in under 500ms, paired with a character-stream bracket-counting filter that dynamically repairs truncated text inputs into valid JSON schemas. Experimental validation proves CareerMind maintains absolute interface stability during cloud outages while providing zero-cost, real-time portfolio gap analyses and interactive interview preparation.