Hiring teams drown in resumes while job requisitions vary wildly in structure. This talk unveils a production-grade Applicant Ranking AI Suite that parses job descriptions and resumes into ontology-aligned structure and produces auditable, explainable rankings. The system combines LangGraph multi-node agents (job + resume), hybrid LLM/embedding similarity across work experience, education, and skills, and a stability penalty (tenure, gaps) to calibrate a final 1–5-star score. We also show how to design for compliance with NYC Local Law 144—including measurable impact ratios for bias audits, public transparency artifacts, and built-in notice/reporting hooks—so teams can operationalize fair, reviewable AI in hiring. Under the hood: Pydantic-guardrailed prompts, per-feature similarity matrices, MLflow-tracked latency/cost, and config-only model swaps (OpenAI/Gemini/Bedrock).