AI Technical Interview Agent

Overview

AI Technical Interview Agent is an intelligent interview automation platform that simulates technical interviews using Google's Gemini API. The application generates interview questions based on the selected job role, evaluates candidate responses, assigns scores, and generates a comprehensive interview report.

The project aims to simplify the initial candidate screening process by providing recruiters with a structured and consistent evaluation system while allowing candidates to practice technical interviews in an interactive environment.

Problem Statement

Recruiters often spend significant time conducting preliminary technical interviews to assess candidates' knowledge and communication skills. Manual screening becomes increasingly difficult as the number of applicants grows.

This project addresses this challenge by automating the first stage of the interview process through AI-generated technical questions and intelligent response evaluation.

Features

1) AI-generated interview questions based on selected job role
2) Multiple interview domains including:
- Python Developer
- Backend Developer
- Machine Learning Engineer
- Data Scientist
3) Automated answer evaluation
4) AI-generated feedback for every response
5) Candidate scoring system
6) Overall interview performance report
7) Interactive web interface built with Streamlit
8) REST API powered by FastAPI
9) Modular backend architecture

Technology Stack

Backend

Python
FastAPI

Frontend

Streamlit

Artificial Intelligence

Google Gemini API
Prompt Engineering

Database

Python-based interview data storage

Tools

Git
GitHub
VS Code

Project Architecture

User
      │
      ▼
Streamlit Frontend
      │
      ▼
FastAPI Backend
      │
      ▼
Gemini API
      │
      ▼
Question Generation
      │
      ▼
Answer Evaluation
      │
      ▼
Performance Report

Workflow

1) User selects the desired interview role.
2) Gemini generates role-specific technical interview questions.
3) Candidate answers each question through the Streamlit interface.
4) The backend sends responses to Gemini for evaluation.
5) AI assigns scores and generates detailed feedback.
6) After the interview, the system creates a final performance report summarizing the candidate's strengths and areas for improvement.

Challenges Faced

While building this project, several technical challenges were addressed:
- 1) Integrating Google's Gemini API into a FastAPI backend.
- 2) Designing prompts to generate relevant interview questions.
- 3) Creating meaningful evaluation criteria instead of generic AI responses.
- 4) Managing session state during the interview process.
- 5) Connecting the Streamlit frontend with FastAPI through REST APIs.
- 6) Organizing the project into modular backend components for better maintainability.

Learning Outcomes

This project strengthened my understanding of:

1) FastAPI application development
2) REST API design
3) Streamlit frontend development
4) Google Gemini API integration
5) Prompt Engineering
6) AI-powered application development
7) Python project structuring
8) Client-server communication
9) Session state management

Future Enhancements

Future improvements planned for the project include:

1) Voice-based interview support
2) Resume analysis before interview generation
3) Authentication and user accounts
4) Recruiter dashboard
5) Candidate interview history
6) Exportable PDF interview reports
7) Deployment on cloud platforms
8) Support for additional technical roles

Conclusion

AI Technical Interview Agent demonstrates how Large Language Models can streamline the technical interview process through intelligent question generation, automated evaluation, and structured reporting. The project combines modern backend development with generative AI to create a practical candidate evaluation platform.