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Building a Serverless AI-Powered Resume Analyzer with AWS

Learn how to build a serverless resume analyzer that extracts useful information from resumes and helps match candidates with job requirements using AWS services.

Finding the right job can be challenging when a candidate has to compare their resume with dozens of job descriptions. A resume analyzer can simplify this process by automatically extracting important information, identifying skills, and comparing them with a job description.
In this article, we will explore how to build a simple serverless resume analyzer using AWS services.
Architecture
A possible architecture for this application is:
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User
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v
Amazon S3
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v
AWS Lambda
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+----> Amazon Comprehend
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+----> Job Matching Logic
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v
Amazon DynamoDB
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v
API Gateway
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v
Web Application
The architecture uses managed AWS services, which means there is no need to maintain traditional servers.

AWS Services Used

Amazon S3

Amazon S3 can store uploaded resumes and other documents.
When a candidate uploads a resume, the file can be stored in an S3 bucket. The application can then trigger additional processing.

AWS Lambda

AWS Lambda acts as the main processing layer.
A Lambda function can:
  • Read the uploaded resume
  • Extract text from the document
  • Process candidate information
  • Identify relevant skills
  • Compare skills with a job description
  • Store the analysis results
Because Lambda is serverless, you only pay when the function executes.

Amazon Comprehend

Amazon Comprehend can be used to analyze the extracted text and identify useful information.
For example, it can help identify:
  • Skills
  • Organizations
  • Locations
  • Key phrases
  • Important entities
This can reduce the amount of custom natural-language-processing logic that developers need to implement.

Amazon DynamoDB

DynamoDB can store the analysis results.
A simple record could contain:
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{
"candidateId": "C001",
"skills": [
"JavaScript",
"React",
"Node.js",
"MongoDB"
],
"matchScore": 82,
"missingSkills": [
"AWS Lambda"
]
}
This allows the application to quickly retrieve previous analyses.

Amazon API Gateway

API Gateway can expose REST APIs for the frontend application.
For example:
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POST /upload-resume
POST /analyze
GET /results/{candidateId}
The frontend can communicate with these APIs without directly accessing the backend services.

How the Workflow Works

The complete workflow can be divided into several steps.

1. Upload the Resume

The candidate uploads their resume through a web application.
The file is stored in an Amazon S3 bucket.

2. Process the Resume

An event can trigger an AWS Lambda function after the file is uploaded.
The Lambda function processes the document and extracts its text.

3. Analyze the Content

The extracted text can be sent to Amazon Comprehend to identify entities and important phrases.
The application can then create a structured list of candidate skills.

4. Compare Skills

The candidate's skills can be compared against the required skills from a job description.
For example:
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Candidate Skills:
JavaScript
React
Node.js
MongoDB

Required Skills:
JavaScript
React
Node.js
AWS
Docker
Kubernetes
The application could identify:
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Matching Skills:
JavaScript
React
Node.js

Missing Skills:
AWS
Docker
Kubernetes
A simple matching score can then be calculated.

5. Store the Results

The final analysis can be stored in DynamoDB.
The frontend can retrieve these results through API Gateway.

Why Serverless?

A serverless architecture is useful for applications like this because the workload can be unpredictable.
For example, there might be:
  • 10 resume uploads during normal periods
  • Hundreds of uploads during a campus placement drive
  • Very few requests during other periods
With AWS Lambda, the application can scale automatically without requiring developers to continuously manage servers.

Improving the Application

The basic application can be extended with additional features.
Some possibilities include:
  • Resume recommendations
  • Job recommendation systems
  • Skill-gap analysis
  • Candidate ranking
  • Resume improvement suggestions
  • Interview preparation recommendations
  • Automated candidate dashboards
Generative AI can also be added to provide more detailed explanations and personalized recommendations.

Security Considerations

Resumes contain personal information, so security should be considered from the beginning.
Some important practices include:
  • Use IAM policies with least-privilege permissions.
  • Keep S3 buckets private.
  • Encrypt stored data.
  • Protect APIs using appropriate authentication.
  • Avoid storing unnecessary personal information.
  • Monitor access using AWS logging and monitoring services.
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Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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