Build and Deploy an AI Resume Analyzer with OpenAI and Azure

What is an AI Resume Analyzer?

An AI Resume Analyzer is a tool that uses artificial intelligence, especially Natural Language Processing (NLP) and machine learning, to evaluate how well a candidate's resume aligns with a given job description. Unlike traditional Applicant Tracking Systems (ATS) that rely completely on keyword matching, AI-powered resume analyzers can understand context, semantics, and even provide actionable feedback to help candidates improve their resumes.

What Problem does an AI Powered Resume Analyzer Solve?

When applying for various jobs, candidates often submit resumes that never make it past automated filters. These filters:

• Miss contextually relevant content

• Fail to suggest improvements

• Are not accessible to job seekers for proactive analysis

This AI Resume Analyzer project solves these problems by allowing job seekers to:

• Upload a resume and job description

• Analyze alignment using LLM-based embeddings

• Identify gaps in skills and experience

• Receive custom improvement suggestions

It provides applicants with clarity, confidence, and control over their job applications.

Different Ways to Build an AI Resume Analyzer Project

There are four primary approaches to implementing a resume analyzer, each varying in complexity, intelligence, and output quality:

1. Keyword Matching (Basic Resume Filtering)

This method involves scanning resumes and job descriptions for exact keyword overlaps (e.g., “Python,” “data analysis,” “SQL”). It counts how many relevant keywords appear and scores the resume accordingly. It is often used in Traditional ATS (Applicant Tracking Systems).

Example Use Case: Filter out resumes that don’t contain any of the required tools or technologies listed in the job description.

2. TF-IDF + Cosine Similarity (Intermediate NLP Matching)

TF-IDF (Term Frequency–Inverse Document Frequency) converts resumes and job descriptions into weighted term vectors, giving more importance to rarer and meaningful terms. Cosine similarity is then used to compare how close the vectors are.

Example Use Case: Rank a set of resumes in order of closeness to a job description using statistical relevance of terms.

3. Embeddings with LLMs (Contextual Matching – Used in This Project)

This method converts text into semantic vector embeddings using pre-trained models like OpenAI Embeddings or Sentence Transformers. These embeddings capture contextual and semantic meaning, enabling more intelligent comparisons.

Example Use Case: Accurately assess whether a resume means the same thing as the job description, even if it uses different wording.

4. Semantic Gap Analysis + LLM Suggestions (Advanced Personalized Feedback)

In this approach, you combine semantic matching (e.g., with embeddings) with LLM-powered suggestions. Once mismatches are identified, a model like GPT-4 explains what’s missing and provides actionable, personalized tips to improve the resume.

Example Use Case:

A job seeker uploads a resume and a job description. The tool tells them:

• “You're missing experience in cloud platforms like AWS.”

• “Consider adding metrics to your bullet points.”

• “Use stronger action verbs like ‘spearheaded’ instead of ‘worked on’.”

Explore the ProejctPro AI Resume Analyzer Project GitHub Repository to view the AI Resume Analyzer Source Code details.

Description of Resume Analyzer Project with Source Code

This AI-powered resume analyzer project helps job seekers evaluate how well their resume matches a specific job description. Going beyond keyword filters, the system uses OpenAI embeddings and GPT-4 to compute similarity scores and generate personalized feedback. Built with Python and Streamlit, the app is deployed on Azure App Services for scalability and accessibility.

Core Features

• Resume & JD upload support (PDF/Image)

• Text extraction using Tesseract and PDF parsers

• Embedding generation via OpenAI

• Semantic similarity using cosine similarity

• Gap analysis to detect missing skills/experience

• Actionable improvement suggestions using GPT-4

• Interactive UI with Streamlit

• Azure cloud deployment

Tech Stack

• Language: Python 3.10

• Libraries: Langchain, Langchain-OpenAI, Tesseract, Streamlit

• Model: OpenAI Embeddings + GPT-4

• Cloud: Microsoft Azure

Understanding the AI Resume Analyzer Source Code Step-by-Step

1. Data Parsing and Preparation

   • Use PDF/Text parsers to extract text from resumes and job descriptions.

   • Preprocess the text data to remove any irrelevant information and standardize the format.

2. Embedding Generation

   • Generate embeddings for both resumes and job descriptions using pre-trained models like OpenAI embeddings.

   • Ensure embeddings capture the semantic meaning of the text for accurate similarity matching.

3. Similarity Calculation

   • Use cosine similarity to calculate the similarity scores between resumes and job descriptions.

   • Rank resumes based on their similarity scores to identify the best matches.

4. Gap Analysis

   • Analyze the differences between resume content and job requirements.

   • Identify key areas where the resume does not align with the job description, such as missing skills or irrelevant experience.

5. Suggestions Generation

   • Generate personalized suggestions for resume improvement using Large Language Models (LLMs).

   • Provide actionable recommendations like relevant skills, action words, and formatting tips.

6. User Interface Development

   • Build a frontend UI using Streamlit to allow users to upload resumes and job descriptions.

   • Display similarity scores, gap analysis, and improvement suggestions in an easy-to-understand format.

7. Feedback Loop and Continuous Improvement

   • Implement a feedback mechanism where users can rate the suggestions provided.

   • Use this feedback to refine the prompts and improve the accuracy and relevance of suggestions.

8. Deployment

   • Deploy the application on Azure using Azure App Service.

   • Ensure scalability and accessibility for users, allowing them to assess and improve their resumes efficiently.