Problem
Job discovery tools tend to be noisy, repetitive, and hard to audit. I wanted a system that could collect listings from multiple sources, reduce noise, and turn the output into structured review artifacts instead of a chaotic feed.
Systems pipeline
AI Job Aggregation and Review Pipeline
Adapted and extended a local AI-assisted job operations pipeline that scans sources, surfaces reviewable roles, and organizes structured review artifacts for faster decision-making.
TypeScriptNode.jsMarkdownTSVPipeline orchestration
What I Built
This project combines my own build direction with an open-source foundation from the career-ops project. I adapted the system into a local job operations workflow that scans sources, filters roles, suppresses duplicates, and outputs markdown and tracker artifacts designed for human review.
Key Decisions
- Framed the project as a structured pipeline instead of an 'agent project' so the output stayed inspectable and grounded in deterministic review stages.
- Used file-based artifacts and review bundles as the interface because they make each run easy to inspect, diff, and revisit.
- Kept the trust model explicit by preferring valid listing URLs and reviewable output over opaque automation.
Outcome / Value
- Turned multi-source scans into clean, review-oriented artifacts that are easier to act on than raw scraped listings.
- Created a stronger example of orchestration, classification, and operational tooling than a typical frontend-only project.
- Produced a case study that highlights systems thinking, adaptation of open-source software, and practical workflow design.
Screenshots / Artifacts
Visual proof tied to the actual system.
