fake jobs only on site
Back to proof
Job Search HQ
A private AI-assisted operating system for sourcing, scoring, tailoring, validating, and tracking a high-signal job search.
Synthetic public view Career pipeline operating system
Posting intake
Fit score
Resume QA
Human review
Fake job dataATS checksNo auto-apply
overclaim checks
no auto-apply framing
Recruiter answer
What proof does this case study show?
This case study shows AI implementation ability through rubric design, intake structure, resume QA, claim checks, tracker hygiene, and human-in-the-loop decisions.
Role fit
- AI enablement
- GTM workflow automation
- Human-in-the-loop evaluation design
Validation signals
- Fit rubric before tailoring
- Resume and ATS QA gates
- No auto-apply or unreviewed outreach
Problem
A serious job search creates noisy inputs: postings, fit notes, tailored resumes, recruiter context, and follow-up state.
What I Designed
Designed the operating model, scoring rules, resume QA gates, data structure, and human review workflow, then used AI coding assistance to implement scripts and maintain artifacts.
Validation
Added guardrails against wrong-fit applications, ATS parsing issues, and overclaiming so AI assistance stayed accountable to the operator.
Public Handling
The real workflow stays private. Public visuals use fake postings, fake trackers, and anonymized workflow diagrams.
Synthetic artifacts
What the public proof can show safely.
These artifacts describe the workflow shape without exposing private data, credentials, logs, internal strategy, or real customer/user records.
Posting intake -> fit score -> resume QA -> application decision.
Fake job record with scoring reasons, risk notes, and next action.
ATS parsing, claim checks, and human approval before any outreach.
No real postings, personal resume variants, or application data are public.
Positioning note
This work is framed as AI-assisted implementation. My ownership is problem definition, workflow design, evaluation criteria, validation, rollout, user feedback, and adoption. Coding-heavy pieces were built with AI coding assistance.
Good fit for teams that need structured AI workflows with scoring, risk review, and accountable human approval.