public page uses synthetic screenshots only
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Job Search HQ
A private career pipeline operations system for sourcing, scoring, QA, follow-up, and human-reviewed decisions.
scoring and actions stay reviewable
private repo stays redacted
Recruiter answer
What proof does this case study show?
This case study shows AI implementation ability through pipeline operations design, scoring rules, source-of-truth records, local QA gates, and human review.
Role fit
- AI-assisted workflow implementation
- RevOps-style pipeline design
- Human-in-the-loop QA and review
Validation signals
- Source-of-truth records before action
- Resume and packet QA gates
- Human approval before outreach, submission, or follow-up
Interview questions answered
The short version a hiring team should understand.
- Messy workflow
- 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.
- System designed
- A local career pipeline operations system with structured records, fit scoring, status tracking, resume/packet QA, follow-up queues, and human approval before action.
- AI-assisted build
- AI coding assistance helped implement scripts, local UI surfaces, validation commands, and artifact tracking around the operating model.
- Validation
- The workflow checks source-of-truth files, PDF/resume readiness, ATS alignment, status consistency, and next-action previews before outreach or submission.
- Usage/adoption
- The system supports ongoing pipeline triage, packet preparation, review loops, follow-up tracking, and closeout updates.
- Proof signal
- It translates directly to GTM AI Ops and RevOps-style workflow design: scoring, routing, QA gates, human review, and durable records.
- Improve next
- Add a stronger synthetic executive dashboard that shows pipeline health without exposing companies, recruiters, resumes, emails, or local paths.
- Public boundary
- The public version uses synthetic screenshots only. Real applications, resumes, contacts, emails, browser/account data, and private documents stay out of the portfolio.
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.
Problem
A serious pipeline creates too much scattered context: saved opportunities, scoring notes, tailored materials, follow-up dates, validation checks, and decision history.
What I Designed
Designed a career pipeline operating model with structured queues, fit scoring, QA gates, source-of-truth records, packet status, follow-up tracking, and reviewable next actions.
Validation
Kept the workflow grounded in local source-of-truth files, explicit status checks, and human review before outreach, submission, or follow-up work.
Public Handling
The public version uses synthetic screenshots captured from demo mode. The real repo, records, materials, contacts, emails, browser state, and local workspace stay private.
Artifacts and proof
What the public proof can show.
These artifacts make the workflow concrete while keeping private data, credentials, logs, internal strategy, and real customer/user records out of public view.
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.
Positioning note
This is operator-led AI implementation proof: workflow judgment, validation, rollout thinking, adoption, and AI-assisted build support where coding is involved.
Good fit for teams that need someone to turn messy GTM, hiring, or operations workflows into reviewable AI-assisted systems with privacy boundaries.