Automating Candidate Screening: Skill Extraction Patterns
Technical insights into parsing multi-format resume documents, extracting verified candidate qualifications, and computing objective match scores in sub-350ms pipelines.
Gourav Singh
Founder & AI Systems Architect
The Chaos of Unstructured Resume Formatting
Recruiters in high-growth companies receive thousands of resumes weekly across radically different formats: complex multi-column PDFs, graphic-heavy Canva templates, poorly formatted Word documents, and plain text uploads.
Standard PDF text extraction libraries frequently read across column boundaries, causing job titles from column 1 to merge with dates from column 2. At NorAI, we built a layout-aware PDF tokenizer that calculates spatial bounding boxes before text extraction, preserving exact chronological work history.
Multi-Dimensional Qualification Scoring
A single monolithic match score is unhelpful to recruiters. Effective talent screening requires decomposing evaluation into four objective, verifiable dimensions:
1. Hard Technical Competencies (40% Weight): Verified programming languages, frameworks, and domain tooling.
2. Role Experience & Seniority (30% Weight): Relevant years in production environments and engineering scale.
3. Educational & Certification Foundation (15% Weight): Relevant degrees, technical credentials, and continuous learning.
4. Recent Project Velocity (15% Weight): Recency and complexity of shipped production systems.
export interface CandidateScorecard {
overallScore: number; // 0 - 100
qualificationTier: 'Tier-1 Qualified' | 'Tier-2 Review' | 'Unqualified';
dimensions: {
technicalSkills: { score: number; matched: string[]; missing: string[] };
experienceDepth: { score: number; yearsRelevant: number; scaleLevel: string };
education: { score: number; degree: string; verified: boolean };
projectRecency: { score: number; lastActiveYear: number };
};
highlightSummary: string;
recommendationReason: string;
}Bias Mitigation & Compliance Guardrails
Automated hiring tools must adhere to strict equal employment regulations. Our extraction engine implements an automatic PII (Personally Identifiable Information) masking layer.
Before candidate evaluation begins, demographic signals—including candidate photo, candidate name, gender indicators, age references, and residential addresses—are stripped from the evaluation payload, ensuring match scores reflect technical merit alone.
AI Resume Shortlister
Automated candidate screening and match scoring for high-volume hiring teams.