All work
In use by the HR team

AI Resume Analysis for HR Support

An internal AI workflow that reads incoming resumes, extracts structured candidate data, and helps HR review applicants against a role's requirements.

Internal HR workflow · Human review stays in the loop

  • Python
  • LLM Workflows
  • NLP
  • Document Parsing
  • FastAPI

Outcome and proof

The workflow is used internally by the HR team. It turns unstructured resumes into a consistent review surface while keeping the final hiring decision with a human reviewer.

  • In use by the HR team
  • Accepts common resume document formats
  • Produces structured candidate information
  • Keeps evidence visible for human review

Context and problem

HR was reading every incoming resume manually to decide whether a candidate matched a role. The volume made the first pass slow and made it difficult to apply the same criteria consistently across every document.

My role and responsibility

I built the internal workflow that ingests resumes, extracts relevant fields, compares the profile with role requirements, and presents the supporting evidence to HR.

Solution and architecture

  1. Ingest — accept PDF and DOCX resumes, including scanned documents.
  2. Extract — normalize education, experience, skills, certifications, and contact information into structured fields.
  3. Match — compare the candidate evidence with the requirements of a specific role.
  4. Explain — show the evidence behind the assessment.
  5. Review — keep HR responsible for the final decision.

Key engineering decisions

Normalize before comparing

Resume layouts vary widely. Separating document parsing from candidate matching keeps layout differences from becoming part of the decision logic.

Show evidence, not only a score

A single opaque score is not sufficient for a hiring workflow. The review surface keeps extracted evidence visible so HR can verify, challenge, or override the system's assessment.

Keep candidate data out of the public case study

The deployment is an internal system. This case study describes the architecture and workflow without publishing candidate documents, client screens, or private operational metrics.

Verified results

The verified public proof is that the workflow is in use by the HR team and supports a repeatable first-pass review. Processing volumes, time savings, and agreement rates are private internal metrics.

Visual evidence

The workflow diagram above shows the path from resume ingestion to human review without exposing candidate data.

Technology stack

Python, LLM workflows, NLP, document parsing, and FastAPI.