How to Build an AI-Powered Job Architecture System with crewAI and Amazon Bedrock

Most job descriptions are born from copy-paste. A recruiter grabs last year's JD, swaps out a few bullet points, adjusts the title, and posts it. The result: inconsistent leveling, missing skills, salary ranges pulled from gut feel, and qualifications lists that scare away half the qualified candidates. I wanted to see what happens when you throw a team of specialized AI agents at this problem instead. Not one prompt — four agents, each focused on a different slice of job architecture, passing structured data to the next. Market research feeds into skill mapping, which feeds into competency frameworks, which all merge into a final JD that actually holds together. By the end of this tutorial, you will have: A single-prompt baseline that shows what a raw LLM produces for job descriptions A 4-agent crewAI pipeline that chains market research, skill taxonomy, competency framework, and JD composition Pydantic schemas that enforce structured, parseable output from every agent A side-by-side