Build an AI-powered recruitment assistant using Amazon Bedrock

Artificial Intelligence Build an AI-powered recruitment assistant using Amazon Bedrock According to a people management survey of 748 HR leaders, recruiters spend an average of 17.7 hours per vacancy on administrative work. That’s more than two working days per hire. A separate 2024 SmartRecruiters survey found that 45% of talent acquisition leaders spend more than half their working hours on tasks that could be automated. This administrative burden forces superficial screening that overlooks qualified candidates while advancing matches based on formatting and keyword density rather than genuine competency alignment. In this post, we demonstrate how to build an AI-powered recruitment assistant using Amazon Bedrock that brings efficiencies to candidate evaluation, generates personalized interview questions, and provides data-driven insights for human hiring decisions. This post presents a reference architecture for learning purposes — not a production-ready solution. Amazon Bedrock and the AWS services used here are general-purpose tools that customers can combine to support a wide variety of use cases, including recruitment workflows. The architecture demonstrates one possible approach; customers should adapt it to their specific requirements. You learn to deploy specialized AI capabilities for resume parsing, candidate scoring, skill assessment, and interview question generation—with Amazon Bedrock Guardrails providing PII anonymization, prompt attack detection, and bias-related content filtering—all working together through a coordinated serverless architecture. The solution uses the Amazon Bedrock Converse API with Amazon Nova Pro, AWS Lambda for processing, Amazon API Gateway for routing, Amazon DynamoDB and Amazon Simple Storage Service (Amazon S3) for data storage, and Amazon Bedrock Guardrails for responsible AI evaluation. Solution overview The AI candidate screening assistant uses foundation models (FMs) available in Amazon Bedrock to help with candidate evaluation, streamline interview preparation, and provide data-driven insights for hiring decisions. The solution processes resumes with comprehensive analysis, calculates multi-dimensional compatibility scores, and generates personalized interview questions based on job requirements and candidate profiles. The authentication and frontend layer uses AWS Amplify to host the web application and Amazon Cognito for user authentication. Amazon Cognito handles user registration, sign in, and provides JWT tokens that are validated by the Amazon API Gateway Cognito Authorizer on every API request. The backend layer uses Amazon API Gateway to route requests to specialized AWS Lambda functions, with each Lambda function handling a specific workflow. The Lambda functions call the Amazon Bedrock Converse API to perform deep resume analysis, calculate compatibility scores, and generate role-specific interview questions. Architecture diagram The following diagram illustrates the architecture of the AI Recruiting Assistant. The architecture contains the following key sections: Frontend Layer: AWS Amplify hosts a responsive React-based web application that provides recruiters with an intuitive interface for managing job postings, reviewing AI-generated candidate assessments, and accessing personalized interview preparation materials. Security Layer: Amazon Cognito manages user registration and authentication, providing JWT tokens that are validated by the Amazon API Gateway Cognito authorizer on every API request. AWS Identity and Access Management (IAM) roles provide least-privilege access for AWS Lambda functions to interact with storage and AI services. Customers are responsible for properly…