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This study analyzed 2,071 job postings related to interpretation from prominent job search platforms, namely Saramin and JobKorea, utilizing a text-mining approach. The study delved into employer preferences, particularly focusing on 'preferred qualifications' and 'job duties.' Python was used to crawl data from online sources and prompt engineering with the Gemini Pro AI model facilitated the extraction of relevant information. Preprocessed data underwent keyword frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling. An association analysis of co-occurring word pairs further enhanced the understanding of employer demands in the interpretation job market. Employers showed a preference for candidates with industry experience, technical skills, foreign language or job-related majors, and possession of relevant certifications other than language and interpretation skills. Notably, emerging industries such as IT and content creation exhibited a heightened emphasis on industry experience and software utilization skills. As for job duties, ‘interpretation and translation’ accounted for only approximately 9.8% of the total job duties described in all postings.