Application Pipeline
A modular workflow for job discovery, requirements analysis, evidence mapping and controlled application tailoring.
Overview
I built this project as a practical experiment in AI-assisted process automation. The objective was not to let an LLM “apply for jobs for me”, but to decompose a messy, repetitive workflow into stages with explicit inputs, outputs and review points.
The problem
Job searching combines several different tasks: discovery, eligibility checks, duplicate detection, requirements analysis, evidence gathering, terminology matching, document tailoring and application tracking. Doing all of this manually creates repetition and makes it easy to lose the factual source of truth across multiple application versions.
Workflow
Key design decision
Why this matters
The project became an exercise in requirements engineering, information architecture and human-in-the-loop AI. The hardest part was not text generation. It was deciding what data needed to remain authoritative, which steps could be probabilistic, which outputs required validation and how to make the process reusable across different role families.
Current direction
- Structured role taxonomy rather than title-only matching
- Evidence-based requirements mapping
- Application-history deduplication
- Separate master-career record and derived CV variants
- Reusable scoring and review criteria
This portfolio version describes the architecture and design logic. Any personal application data is omitted.