EzhilArasan

Built workforce and organisational analysis across job architecture, spans and layers, reporting relationships, location, and labour cost while strengthening HRIS validation and global HRBP reporting.

Built an AI-enabled employee-listening and early-warning system that managed conversations, maintained longitudinal happiness signals, re-engaged incomplete interactions, and surfaced material sentiment changes for HRBP review. It reached 150+ verified employees in an initial 600+ employee population.

Built an AI-enabled pre-boarding engagement and risk-detection workflow used across 200+ candidates. Engagement signals were associated with approximately 20% lower offer-to-joining drop-off; candidates completing three or more interactions showed roughly 80% observed likelihood of joining.

Developed an AI-driven assessment system used across hundreds of candidate evaluations, combining resumes, interview transcripts, job requirements, and Caliper assessments while independently evaluating interview relevance, coverage, and gaps.

Built a semantic screening and candidate-prioritisation solution that bulk-processed applicant pools, ranked candidates against job requirements, and provided deeper technical-stack matching for large hiring drives and inbound volumes.

Linked historical hiring profiles, skills, source companies, and subsequent employee performance to recommend target employers, keyword combinations, and Boolean searches for recurring and backfill roles.

Built an internal talent-search solution to surface employees by skills and career history, supporting internal mobility, opportunity discovery, and talent identification across the organisation.