AI Interview Coach is a cloud-based application designed to generate personalized interview questions based on job descriptions. It leverages AWS services such as Lambda, SageMaker, OpenSearch, and S3 to provide a seamless experience for users preparing for job interviews.

• Job Description Validation: Ensures the input is valid and stores it securely in S3.
• Question Generation: Uses SageMaker and OpenSearch to generate relevant interview questions based on the job description.
• Responsive Frontend: A React-based user interface for interacting with the application.
• Scalable Backend: Built using AWS Lambda and API Gateway for serverless scalability.

The application is built using a microservices architecture, with the following components:

1. Frontend: A React-based web application hosted on EC2 instances behind an Application Load Balancer (ALB).
2. Backend: AWS Lambda functions for input validation and question generation, integrated with API Gateway.
3. Storage: S3 for storing job descriptions and generated questions.
4. Machine Learning: SageMaker for generating interview questions using a pre-trained model.
5. Search: OpenSearch for querying relevant documents based on the job description.
6. Authentication: Cognito for user authentication and API Gateway authorization.

• Frontend: React, CSS
• Backend: Python (AWS Lambda)
• Cloud Services: AWS (Lambda, SageMaker, OpenSearch, S3, Cognito, API Gateway, CloudFormation)
• Infrastructure as Code: CloudFormation templates for provisioning resources

User uploads the job description taken from websites like LinkedIn, Indeed, or Company job portal

The application returns questions for inteerview preparation based on the job description

• AWS account
• Node.js and npm installed locally
• Docker installed locally (optional for containerized frontend)

1. Frontend:

   • Navigate to the frontend directory.
   • Install dependencies: npm install.
   • Start the development server: npm start.
   • Build for production: npm run build.

2. Backend:

   • Deploy CloudFormation templates in the following order:
     • network.yaml
     • storage.yaml
     • authentication.yaml
     • sagemaker-opensearch.yaml
     • api-gateway-lambda.yaml
     • frontend.yaml

3. Dockerized Frontend:

   • Build the Docker image: docker build -t ai-interview-coach ..
   • Run the container: docker run -p 3000:3000 ai-interview-coach.

• Method: POST
• Description: Validates the job description and stores it in S3.
• Request Body:

  {
    "query": "Job description text"
  }