<p>Recent recruitment trends are shifting from traditional reactive recruitment toward proactive talent acquisition, particularly in the IT industry, where the demand for highly skilled developers is rapidly growing. While the integration of artificial intelligence (AI) into human resource management (HRM) has reshaped the recruitment landscape, most previous attempts to leverage AI in recruitment have focused on supporting traditional reactive recruitment. This study proposes a novel approach for automating the proactive talent acquisition process for qualified developers. The proposed approach, called LibMatch, automatically produces a list of qualified developers who meet the requirements of hiring companies given a job posting as input. Central to the proposed approach are pre-trained language models, including KeyBERT and SentenceBERT, which are used to extract critical terms from job descriptions and match them to relevant technology libraries that developers have used. The developer candidates are extracted from GitHub, the largest and most popular social coding platform, with more than 100 million developers worldwide. An illustrative example of talent acquisition for natural language processing engineers is presented to demonstrate and validate the proposed approach. The proposed approach enables recruiters to efficiently and effectively identify qualified candidates from a talent ocean without any additional effort beyond entering job postings. The code is available at <a href="https://github.com/Seoultech-Inno/LibMatch">https://github.com/Seoultech-Inno/LibMatch</a>.</p>