JobSphere: An AI-Powered Multilingual Career Copilot for Government Employment Platforms
This work addresses accessibility gaps for Punjab/Hindi-speaking users in rural areas by enhancing a specific government employment platform, representing an incremental improvement with domain-specific impact.
The paper tackles engagement and accessibility challenges on government employment platforms by introducing JobSphere, an AI-powered multilingual career assistant for Punjab's PGRKAM platform, which achieves a 68% precision@10 score for job recommendations, 94% factual accuracy, and a 50% improvement in usability scores.
Users of government employment websites commonly face engagement and accessibility challenges linked to navigational complexity, a dearth of language options, and a lack of personalized support. This paper introduces JobSphere, an AI-powered career assistant that is redefining the employment platform in Punjab called PGRKAM. JobSphere employs Retrieval-Augmented Generation (RAG) architecture, and it is multilingual, available in English, Hindi and Punjabi. JobSphere technique uses 4-bit quantization, allowing the platform to deploy on consumer-grade GPUs (i.e., NVIDIA RTX 3050 4GB), making the implementation 89% cheaper than that of cloud-based systems. Key innovations include voice-enabled interaction with the assistant, automated mock tests, resume parsing with skills recognition, and embed-based job recommendation that achieves a precision@10 score of 68%. An evaluation of JobSphere's implementation reveals 94% factual accuracy, a median response time of 1.8 seconds, and a System Usability Scale score of 78.5/100, a 50% improvement compared to the baseline PGRKAM platform context. In conclusion, JobSphere effectively fills significant accessibility gaps for Punjab/Hindi-speaking users in rural locations, while also affirming the users access to trusted job content provided by government agencies.