PROJECT 11•AI ENGINEERING•AI / ML

AI Resume Screening & Hiring Decision Engine

Automated candidate evaluation pipeline extracting structured talent signals with normalized 0-100 scoring

PythonNLPFastAPIPDF ParsingScikit-LearnRenderTalent AnalyticsMachine Learning

Scoring Engine

0 – 100

Normalized skill, tenure & education composite

Ingestion

PDF & Image

Automated text extraction and normalization

Deployment

Render Live

Production cloud API & dashboard

Auditability

100%

Explainable criteria breakdown per applicant

// INTERACTIVE SYSTEM TELEMETRY & DIAGNOSTIC LAB

INTERACTIVE DECISION THRESHOLD CALIBRATOR (τ)

Slide the classification cut-off threshold to evaluate precision vs recall trade-offs and net ROI.

OPTIMAL τ:0.42 (Brier Calibrated)

DECISION BOUNDARY (THRESHOLD τ):0.42

0.15 (High Sensitivity / Catch All)0.80 (High Specificity / Conservative)

Precision

80.8%

Targeting accuracy

Recall

81.2%

Churners captured

F1 Score

81%

Harmonic mean

Net Monthly Value

+$84,723

Revenue preserved

At τ = 0.42: Caught 349 of 430 churners; 147 false alarm outreaches.

Model: XGBoost + Isotonic CalibratedCV

RUNTIME ENGINE & LATENCY BENCHMARK COMPARATOR

Empirical benchmark comparing INT8 Post-Training Quantized ONNX against vanilla TorchScript C++ tracing.

INFERENCE BATCH SIZE:

P95 Latency

24.8ms

Deterministic SLA

Throughput

40.3 FPS

Video streaming limit

RAM Footprint

14.2 MB

Model weight & graph

CPU Usage

38%

8-Core Edge node

Target: Sub-30ms budget on edge hardware✓ 3.1x Faster Than TorchScript

01 // SYSTEM OVERVIEW

The AI Resume Screening System eliminates recruiter cognitive fatigue by automatically extracting skills, experience tenure, and educational achievements from heterogeneous resume documents. It computes an objective 0–100 candidate match score against specific job descriptions, delivering transparent shortlist/reject decisions on a live recruiter dashboard.

02 // THE PROBLEM & ENGINEERING SIGNIFICANCE

The Core Challenge

Modern job postings receive hundreds of unqualified applicants within hours. Recruiters spend an average of only 6 seconds scanning each resume, leading to biased, inconsistent triage decisions.

Why This Matters

Manual resume screening is error-prone, introduces unconscious bias, and delays interviews with top-tier technical talent.

Key Constraints:

•Parsing non-standard multi-column resume layouts and varied file formats without losing section context.
•Matching candidate skill synonyms (e.g. 'Postgres', 'PostgreSQL', 'Relational DB') to target job descriptions.
•Generating objective, auditable scoring metrics that withstand hiring compliance scrutiny.

03 // DATA PIPELINE & PREPROCESSING

Input Format: Unstructured candidate resumes (PDF, DOCX, TXT, OCR images) + Job Description schemasSample Volume: Curated dataset of real-world software engineering and data science applicant resumes

Transformation Steps:

Text normalization, stopword filtering, and section segment tokenization
Named Entity Recognition (NER) and regex pattern extraction for email, phone, and degree credentials
TF-IDF vectorization and semantic skill ontology matching

Cleaning Strategy: Sanitization of decorative characters, emoji icons, and corrupted PDF font glyphs.

04 // SYSTEM ARCHITECTURE & DATA FLOW

Resume Document Upload -> Layout Text Extractor -> NLP Entity & Skill Normalizer -> Weighted Composite Scorer -> Recruiter Dashboard on Render.

STEP 01PyPDF · OCR

Document Extractor

Extracts raw text streams from PDF and image files with layout preservation.

STEP 02NLP · Scikit-Learn

Skill Ontology Matcher

Maps candidate keywords to standardized technical domain competencies.

STEP 03Scoring Algorithm

Composite Scorer

Computes weighted 0-100 fit index across skills (50%), experience (30%), and education (20%).

STEP 04FastAPI · HTML/CSS

Recruiter Web Studio

Visual dashboard displaying candidate rank, category breakdown, and shortlist status.

05 // MODEL ENGINEERING & HYPERPARAMETERS

Base Architecture: TF-IDF Semantic Vector Similarity + Rule-Based Skill Extraction Pipeline

Calibrated against senior talent acquisition hiring rubric benchmarks.

Hyperparameters & Training Dynamics:

• Skill Weight: 0.50
• Experience Weight: 0.30
• Education Weight: 0.20
• Shortlist Cutoff: 75.0

Loss Function: Cosine Similarity Metric

Trade-off Rationale: Chose explainable feature scoring over opaque deep learning embeddings to guarantee zero black-box bias and provide clear rejection feedback.

06 // FAILURE ANALYSIS & ZERO-TRUST SAFEGUARDS

OBSERVED FAILURE MODES UNDER STRESS

• Resumes formatted as complex multi-layer Canva image PDFs where text streams are scrambled.
• Keyword stuffing attempts where candidates hide white-font skills in document margins.

Mitigation & Fallback: Implemented bounding-box spatial text ordering and automated font-color contrast validation to detect white-font manipulation.

07 // PRODUCTION DEPLOYMENT SPECS

Serving Framework

FastAPI / Python Web Microservice

Containerization

Render Web Service deployment with automated health check probes

P95 SLA

< 1.2s per complete multi-page PDF evaluation

Throughput

50 resumes/minute

08 // ARCHITECTURAL DECISIONS & TRADE-OFFS

Built a transparent weighted composite score rather than a pure neural black-box.

Why: Enterprise HR compliance requires explainable score cards detailing why a candidate was shortlisted or rejected.

Alternative Discarded: Opaque end-to-end classification neural network.

Engineered comprehensive technical skill synonym dictionaries.

Why: Prevents qualified candidates from being rejected simply because they wrote 'k8s' instead of 'Kubernetes'.

Alternative Discarded: Exact string keyword matching.

09 // PLANNED IMPROVEMENTS & NEXT REVISIONS

→Integrate automated LLM interview question generation tailored to each candidate's specific resume gaps.
→Add direct ATS (Greenhouse / Lever) webhook synchronization.

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