Digital and data-intensive hiring in AAU research libraries: Domains, competencies, and AI responsibilities

Academic research libraries recruit across systems, metadata, digital preservation, research data, computational scholarship, open research, user experience, and artificial intelligence (AI), but workforce studies usually examine these specialties separately. This study analyzes 413 digital and data-intensive job advertisements from 65 Association of American Universities institutions, published between January 2023 and May 2026 and recovered primarily through Code4Lib and IASSIST, plus three from institutional library pages. Deductive-inductive content analysis assigned each advertisement one primary occupational domain and multiple competency dimensions. A second coder assessed reliability in a stratified sample of 104 advertisements and all 51 containing explicit AI terminology. Systems, software, and infrastructure was the largest domain (29.1%), followed by metadata, e-resources, and knowledge organization (18.9%). Most advertisements combined specialist expertise with instruction, partnership, project coordination, and strategic responsibility. AI-centered titles were uncommon; explicit implementation and governance responsibilities appeared across established domains. In the qualification-assessable subset, credential language indicated multiple professional and technical pathways. Salary medians varied by position level but were unadjusted for institutional or geographic context. Within this specialist-channel corpus, employers more often placed explicit AI responsibilities in established roles than in AI-centered titles. The findings describe advertised expectations, not enacted duties or complete institutional workforce strategies.