How do you implement AI-powered resume screening and talent acquisition?

AI resume screening automates candidate evaluation while mitigating hiring bias. Architecture: resume parsing (extract structured data: contact info, experience, education, skills from PDF/DOCX using NLP), skills matching (compare extracted skills against job requirements using embedding similarity), experience scoring (evaluate relevance and depth of work history), and ranking (produce a composite score for recruiter review). Implementation: use NER models trained on resume entities (job titles, company names, skills, degrees, certifications), implement skills taxonomy mapping (standardize skill variations: "JS", "JavaScript", "ECMAScript" map to the same canonical skill), and compute match scores between candidate profiles and job descriptions using embedding similarity. Bias mitigation: remove identifiable information before scoring (name, address, graduation year, photos), audit model predictions across demographic groups, implement fairness constraints in the ranking algorithm, and regularly test for adverse impact. EEOC compliance: maintain records of all candidates and disposition reasons, ensure AI screening doesn't create disparate impact, provide human review of AI-generated rankings, and allow candidates to request explanation of automated decisions. Advanced features: automated sourcing (match passive candidates from talent pools), interview scheduling automation, candidate communication sequences, and talent pipeline management. Integration: connect with ATS (Applicant Tracking Systems: Greenhouse, Lever, Workday), job boards (LinkedIn, Indeed), and HR systems. Quality metrics: track candidate quality through the hiring funnel, measure diversity metrics at each stage, compare AI screening accuracy against human screening, and monitor offer acceptance rates per AI score band.