High-signal pipeline work has noisy sources, changing role details, tailored documents, follow-up timing, and many places for stale records or weak claims to slip in.
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AI Workflow / Career Pipeline Operations
Job Search HQ
A private career pipeline operations system for sourcing, scoring, QA, follow-up, and human-reviewed decisions.
Outcome: Created a private local workflow that keeps sourcing, review, tailored materials, validation, follow-up tracking, and decision history organized without making private records public.
The evidence
Problem, system, review loop, result.
A local career pipeline operations system with structured records, fit scoring, status tracking, resume/packet QA, follow-up queues, and human approval before action.
The workflow checks source-of-truth files, PDF/resume readiness, ATS alignment, status consistency, and next-action previews before outreach or submission.
The system supports ongoing pipeline triage, packet preparation, review loops, follow-up tracking, and closeout updates.
Public proof
It translates directly to GTM AI Ops and RevOps-style workflow design: scoring, routing, QA gates, human review, and durable records.
The public version uses synthetic screenshots only. Real applications, resumes, contacts, emails, browser/account data, and private documents stay out of the portfolio.
public page uses synthetic screenshots only
scoring and actions stay reviewable
private repo stays redacted
Ownership
Clear about the judgment. Clear about the assistance.
Problem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.
Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.
Artifacts and proof
Look at the work.
Screenshots and artifacts are public-safe by design. Private strategy, records, credentials, and customer data stay out.
Fake companies, fake roles, fake statuses, and fake review notes show the operating model without exposing real records.
Example scoring and next-action surfaces show how noisy opportunities are turned into a prioritized workflow.
Synthetic packet status demonstrates document QA and readiness gates without showing real resumes or submissions.
Simulated local commands show the review step before any script or workflow action is run.
Why it matters