AI Interview Agent: Intelligent Automated Interviewing
Project Overview
Developed a sophisticated AI interview agent that simulates job interviews by leveraging large language models and retrieval-augmented generation. The system creates personalized interview experiences by analyzing resumes and job descriptions to generate relevant questions and evaluate candidate responses.
Technical Architecture
- Implemented Retrieval-Augmented Generation (RAG) to provide the agent with contextual knowledge
- Utilized LangChain and LangGraph frameworks for building agent workflows and reasoning capabilities
- Integrated Llama 3 as the foundation model for natural language understanding and generation
- Created a vectorization system for processing resumes and job descriptions
- Designed an LLM-as-a-judge module for objective evaluation of candidate responses
Key Features
- Resume Analysis: Automatically extracts key information from candidate resumes
- Job Description Matching: Cross-references resume content with job requirements
- Dynamic Question Generation: Creates personalized interview questions based on candidate background
- Voice-Interactive Conversations: Conducts spoken interviews with natural dialogue flow
- Context-Aware Follow-ups: Adapts questioning based on previous responses
- Structured Assessment: Evaluates responses against predefined criteria and job requirements
Technologies Used
- LangChain
- LangGraph
- Llama 3
- NLP techniques
- Vector databases
- Generative AI
- Agentic AI architecture
Impact
This system streamlines the interview process, provides consistent evaluation criteria, and offers candidates practice opportunities without human intervention. The agent’s ability to personalize questions and adapt to responses creates a natural interview experience while reducing bias in the hiring process.