Building an AI Interview Evaluator for HR Tech

Overview

In this blog, we’ll walk through building a minimum viable product (MVP) of an AI-powered interview evaluator. This includes parsing resumes, scoring candidates based on job-fit, and even generating tailored interview questions using AI. We’ll use tools like Python, OpenAI, and simple cloud services to build something functional without needing a PhD in AI.

1. Introduction: Why Automate Interviews?

Hiring at scale is hard — especially when you’re sifting through hundreds of resumes to find a handful of promising candidates. Traditional hiring pipelines often rely on manual screening, which is slow, inconsistent, and prone to human bias.

An AI-powered interview evaluator can:

• Reduce screening time significantly.
• Provide consistent candidate evaluations.
• Assist hiring managers with dynamic question generation.

In this guide, we’ll break down the steps to build a working MVP that can score resumes and generate smart interview questions based on job descriptions and candidate profiles.

2. Defining the MVP Scope

Our MVP will handle the following:

• Input: A job description and candidate resumes.
• Output: A score for each resume and a set of suggested interview questions.

Scoring System

We’ll use a weighted system:

• Resume Keyword Matching (35%): Basic parsing and keyword detection.
• Rule-Based Evaluation (35%): Experience, education, certifications.
• AI-based Scoring (30%): LLMs (like GPT-4) to judge context and relevance.

Bonus features:

• Generate interview questions for both HR and technical rounds.
• Output CSV of scored candidates with feedback.

3. Phase 1: Resume Parsing & Keyword Matching (35%)

Tools Needed:

• Python
• pdfminer.six or PyMuPDF for PDF parsing
• spaCy for NLP keyword detection

Logic:

1. Parse text from uploaded resumes (PDF/DOC).
2. Extract tokens and lemmatize using spaCy.
3. Match against keyword bank generated from job description.

from pdfminer.high_level import extract_text
import spacy

nlp = spacy.load("en_core_web_sm")

keywords = ["Python", "Django", "REST API", "AWS"]
resume_text = extract_text("resume.pdf")
doc = nlp(resume_text)

matched_keywords = [token.text for token in doc if token.text in keywords]
score = (len(matched_keywords) / len(keywords)) * 35  # out of 35

We’ll store this partial score for the final evaluation.

4. Phase 2: Rule-Based Scoring (35%)

Logic:

Define a few rules:

• Minimum years of experience = 2 (10 points)
• Degree match = 10 points
• Certifications (AWS, etc.) = 5 points
• Projects mentioned = 10 points

Each resume is checked against these and given marks.

rule_score = 0
if "2+ years" in resume_text:
    rule_score += 10
if "B.Tech" in resume_text or "Bachelor" in resume_text:
    rule_score += 10
if "AWS Certified" in resume_text:
    rule_score += 5
if "project" in resume_text.lower():
    rule_score += 10

Total out of 35 again.

5. Phase 3: AI Scoring Using LLMs (30%)

Tool:

• OpenAI API (GPT-4)

Prompt Example:

import openai

openai.api_key = "YOUR_API_KEY"

prompt = f"""
You're an AI recruiter. Given the job description below and the candidate's resume, score the fit on a scale of 1 to 30.

Job Description:
{job_description}

Resume:
{resume_text}
"""

response = openai.ChatCompletion.create(
  model="gpt-4",
  messages=[
    {"role": "user", "content": prompt}
  ]
)
ai_score = int(response["choices"][0]["message"]["content"].strip())

Combine with earlier scores to get final score out of 100.

6. Generating Interview Questions Dynamically

Logic:

Use OpenAI again to generate role-based questions.

question_prompt = f"""
Generate 5 HR and 5 technical interview questions for the following job description:

{job_description}
"""

response = openai.ChatCompletion.create(
  model="gpt-4",
  messages=[
    {"role": "user", "content": question_prompt}
  ]
)
questions = response["choices"][0]["message"]["content"]

7. Building a Simple UI

• Use Streamlit or Gradio for quick UI
• Upload job description + resumes
• Display scored candidates + questions

8. Deployment Options

• Local testing: Use Streamlit + Python env
• Cloud: Deploy on Render, Vercel, or AWS EC2
• Storage: Use S3 for resumes if needed

9. Challenges, Ethics & Limitations

• AI bias in scoring (e.g., over-indexing certain keywords)
• Resume format variability
• LLM cost and rate limits
• Avoiding over-automation in final decisions

10. What’s Next After MVP?

• Add feedback loop (track hired candidates)
• Use vector embeddings for deeper similarity search
• Integrate with ATS tools (like Greenhouse, Zoho)
• Build dashboards and analytics