I chose this problem because hiring affects real people, so the safeguards deserve as much attention as the automation.
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Engineering case study · AI Systems Engineering · 2026
AI Hiring Team
A candidate evaluation pipeline with deliberate safety constraints built in from the design stage.
Central idea
What this system investigates.
AI can support hiring analysis, but it should not hide uncertainty or make the final employment decision.
Problem
Why this system exists.
Candidate evaluation tools process sensitive data and influence consequential decisions. A responsible prototype needs explicit safeguards around scoring logic, data handling, prompt injection, access control, and the boundary between automated assessment and human judgment.
System architecture
How the system is organized.
- Analyze role
- Parse candidates
- Screen and evaluate
- Compute score
- Flag uncertainty
- Generate review report
Implementation
Methods and engineering choices.
Five agents perform bounded assessment stages across role analysis, CV parsing, screening, evaluation, and flagging. Deterministic Python computes the composite score. Inputs are screened for prompt injection before reaching agents. Fallback outputs and low-confidence assessments are flagged for mandatory human review.
Results and evidence
What the current record demonstrates.
- Deterministic weighted scoring computed by Python eliminates model-generated arithmetic as a failure mode
- Prompt-injection screening on all candidate inputs and escaped content in generated reports
- Optional encryption and data retention controls address candidate data handling at rest
- Every generated report explicitly states the system supports rather than replaces human hiring decisions
Verification
Checks performed and their scope.
- Deterministic Python computes the composite score rather than delegating arithmetic to the model.
- Prompt-injection screening, fallbacks, low-confidence flags, and human-review requirements are explicitly implemented.
Limitations
What this work does not yet establish.
- The prototype should support rather than replace human hiring decisions.
- A full human-review UI and bias/reliability evaluation are still required before real-world deployment.
Engineering decisions
Decisions that define the model boundary.
- Excluded unverified background research from the composite score to keep scoring logic auditable.
- Made parse fallbacks and low-confidence flags visible rather than silently assigning neutral scores.
- Documented the absence of a human-review UI as a current limitation, not a planned feature.
A note from the build
What stayed with me.
A neat final score can hide messy inputs, prompt injection or a low-confidence reading of a CV. The number can look more objective than the process that produced it.
I learned to make deterministic scoring, uncertainty flags and human review explicit—and not to treat a prototype like this as ready to make hiring decisions on its own.
Next step
What I would investigate next.
Build an explicit human-review interface, add model and prompt audit logging, and conduct bias and reliability evaluation before any real-world deployment.
Source and related work