A Scalable AI-Driven Mobile Framework for Automated Job Matching and Candidate Alignment

Author(s)	Chanthuru M, Balamurugan M, Arunkumar S, Chandru M, Mohanapriya M
Country	India
Abstract	Contemporary recruitment processes encounter significant inefficiencies stemming from manual workflow dependencies, evaluative inconsistencies, and constrained scalability. This research proposes an AI-enabled mobile framework that automates candidate-job alignment through machine intelligence and system optimization strategies. The architecture incorporates graph-based task orchestration, reference-tracked resource deallocation, demand-responsive execution, predictive workload modeling, and adaptive scaling mechanisms. Performance evaluations reveal matching precision between 87% and 90%, response latencies below two seconds, and 25% memory reduction through context-aware resource governance. The solution offers an autonomous platform appropriate for enterprise-scale talent acquisition operations.
Keywords	Recruitment Automation, Machine Learning, Dynamic Scaling, Computational Linguistics, Cross-Platform Architecture
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-14
DOI	https://doi.org/10.63363/aijfr.2026.v07i02.4926

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