I built a REST API that parses job descriptions into structured JSON using Claude Haiku, here's how...

Problem Statement Every ATS integration I've worked on hits the same wall. Job descriptions are written for humans. Salary buried in paragraph 4. Skills scattered across three sections. "Competitive compensation DOE" instead of a number. No two companies format them the same way. I kept writing regex and spaCy pipelines to extract structured data from JDs, and they kept breaking on edge cases. Eventually I stopped fighting it. The Solution: Let the LLM Handle It I built JD Parser Pro — a REST API that takes raw job description text and returns clean, structured JSON in under 3 seconds. You can try it right now with no signup: bashcurl -s -X POST https://jd-parser-api.onrender.com/v1/parse \ -H "Content-Type: text/plain" \ -d 'Senior Data Engineer at Stripe, San Francisco, CA. Salary: $160,000 - $220,000/year. Full-time. 5+ years Python, Spark, dbt required. Kafka and Airflow preferred. Remote-friendly. Bachelor degree in CS required. Benefits: Equity, 401k, medical, dental, vision, unl