11. Build an AI Resume & Interview Platform
Introduction
Section titled “Introduction”Build an AI-powered resume and interview platform that parses resumes, analyzes skills and gaps, generates an ATS compatibility score, and conducts realistic mock interviews with AI-generated questions and feedback.
Job hunting is stressful. An AI platform that provides unbiased resume analysis, skill gap identification, and realistic interview practice helps candidates prepare effectively while helping recruiters screen efficiently.
Problem Statement
Section titled “Problem Statement”Job seekers apply to hundreds of positions without knowing how their resume performs. An AI resume and interview platform should:
- Parse resumes and extract structured information
- Analyze skill match against job descriptions
- Generate ATS compatibility scores with improvement suggestions
- Conduct voice/text mock interviews with AI interviewer
- Provide detailed feedback on interview performance
Business Use Case
Section titled “Business Use Case”A career services company needs a platform that helps 50K+ students prepare for jobs — analyzing resumes against real job listings, practicing interviews with AI, and tracking improvement over time.
Requirements
Section titled “Requirements”Functional Requirements
Section titled “Functional Requirements”| # | Feature | Description |
|---|---|---|
| FR1 | Resume parsing | Extract skills, experience, education, projects |
| FR2 | Skill analysis | Map skills to job requirements, identify gaps |
| FR3 | ATS scoring | Score resume compatibility with job descriptions |
| FR4 | Improvement suggestions | Specific recommendations for resume optimization |
| FR5 | Coding challenges | Generate and evaluate coding problems |
| FR6 | Behavioral questions | Generate situational questions |
| FR7 | Voice interview | AI interviewer with speech recognition |
| FR8 | Performance feedback | Score communication, technical accuracy, structure |
Non-Functional Requirements
Section titled “Non-Functional Requirements”| # | Requirement | Target |
|---|---|---|
| NFR1 | Resume processing | < 5s per resume |
| NFR2 | ATS accuracy | > 90% correlation with real ATS scores |
| NFR3 | Question relevance | > 85% questions relevant to job |
| NFR4 | Interview quality | Human rating > 4.0/5 |
| NFR5 | Scalability | Handle 1000+ concurrent interviews |
Technology Stack
Section titled “Technology Stack”| Layer | Technology | Purpose |
|---|---|---|
| Frontend | Next.js + Tailwind + WebRTC | Resume viewer, interview UI |
| Backend | FastAPI (Python) | API server, interview orchestration |
| AI | GPT-4o / Claude | Resume analysis, question generation |
| Speech | Whisper (STT) + ElevenLabs (TTS) | Voice interview |
| Database | PostgreSQL | User profiles, interview records |
| Vector DB | pgvector | Job-resume matching |
| pdf.js / PyMuPDF | Resume parsing |
Architecture
Section titled “Architecture”flowchart TD subgraph FRONT["Frontend"] RESUME_UI["Resume Upload\n+ Analysis"] INTERVIEW_UI["Interview Room\nWebRTC Voice"] DASHBOARD["Progress Dashboard"] end subgraph ANALYSIS["Analysis Engine"] PARSE["Resume Parser\nPDF → Structured"] SKILL_MAP["Skill Mapper\nJob market matching"] ATS["ATS Scorer\nCompatibility score"] end subgraph INTERVIEW["Interview Engine"] Q_GEN["Question Generator\nRole-specific"] VOICE["Voice Interface\nSTT + TTS"] EVAL["Performance Evaluator\nReal-time scoring"] FEEDBACK["Feedback Generator\nDetailed report"] end subgraph DATA["Storage"] PG["PostgreSQL"] S3["Resume PDFs"] VDB["pgvector\nEmbeddings"] end
RESUME_UI --> PARSE PARSE --> SKILL_MAP SKILL_MAP --> ATS INTERVIEW_UI --> Q_GEN INTERVIEW_UI --> VOICE VOICE --> EVAL EVAL --> FEEDBACK
style FRONT fill:#3b82f6,color:#fff style ANALYSIS fill:#f59e0b,color:#fff style INTERVIEW fill:#22c55e,color:#fffResume Analysis Pipeline
Section titled “Resume Analysis Pipeline”flowchart LR PDF["Resume PDF"] --> PARSE["Parse\nExtract sections"] PARSE --> STRUCT["Structured Data\nSkills, experience, education"] STRUCT --> MATCH["Match to Job\nRequired vs actual skills"] MATCH --> SCORE["ATS Score\nCompatibility %"] SCORE --> GAPS["Gap Analysis\nMissing skills"] GAPS --> RECOMMEND["Recommendations\nImprovement suggestions"]
style PARSE fill:#3b82f6,color:#fff style SCORE fill:#f59e0b,color:#fff style RECOMMEND fill:#22c55e,color:#fffInterview Flow
Section titled “Interview Flow”sequenceDiagram participant C as Candidate participant AI as AI Interviewer participant Eval as Evaluator
C->>AI: Start interview AI->>AI: Generate questions based on resume + role AI->>C: "Tell me about your experience with React" C->>AI: Response (voice/text) AI->>Eval: Evaluate response Eval->>Eval: Score: technical accuracy, structure, clarity
Note over AI: Dynamic follow-up based on quality
AI->>C: "Can you describe a challenging project?" C->>AI: Response AI->>Eval: Evaluate AI->>C: "How would you design a..."
Note over AI: 15-20 questions total
C->>AI: End interview AI->>C: Generate comprehensive feedback report AI->>C: Scores per category + improvement areasAPI Design
Section titled “API Design”| Method | Endpoint | Purpose |
|---|---|---|
| POST | /api/resume/upload | Upload resume PDF |
| POST | /api/resume/analyze | Analyze resume against job |
| GET | /api/resume/{id}/score | Get ATS score |
| POST | /api/interview/start | Start mock interview |
| POST | /api/interview/{id}/answer | Submit answer |
| GET | /api/interview/{id}/feedback | Get interview feedback |
| POST | /api/interview/{id}/end | End interview |
Evaluation
Section titled “Evaluation”| Metric | Method | Target |
|---|---|---|
| ATS score correlation | Compare with human ATS scoring | > 90% |
| Interview question quality | Candidate rating | > 4.0/5 |
| Feedback accuracy | HR professional review | > 85% |
| Resume parse accuracy | Field extraction accuracy | > 95% |
| User improvement | Score improvement over time | > 20% |
Interview Questions
Section titled “Interview Questions”Q: Design the resume parsing and ATS scoring system.
Pipeline: (1) PDF parsing — Extract text + preserve structure (sections, bullets), (2) Section classification — Identify experience, education, skills, projects sections, (3) Entity extraction — Extract skills (tech + soft), job titles, dates, companies, degrees, (4) Skill normalization — Map to standard skill taxonomy (e.g., “React.js” → “React”), (5) Job matching — Compare extracted skills against job description requirements, (6) Scoring — Weighted score: required skills (60%), preferred skills (30%), experience level (10%), (7) Recommendations — Identify highest-impact additions (most requested missing skills).
Summary
Section titled “Summary”| Feature | Implementation |
|---|---|
| Resume parsing | PDF extract + LLM section classification |
| ATS scoring | Skill matching against job requirements |
| Interview questions | LLM generates role-specific questions |
| Voice interview | WebRTC + Whisper STT + ElevenLabs TTS |
| Performance feedback | Real-time evaluation + detailed report |
| Progress tracking | Score improvement over multiple interviews |
Navigation
Section titled “Navigation”Previous: 10 — Build an AI Research Agent
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