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.