Overview
I integrated Amazon Bedrock into a cloud-based recruiting application to deliver intelligent candidate recommendations. A Nest.js backend and React frontend power prompt workflows for semantic matching against role requirements, using Bedrock foundation models (Claude) to streamline hiring decisions.
Architecture
flowchart TB Recruiter(["Recruiter"]) FE["React frontend"] API["Nest.js API"] Prompts["Prompt workflows"] Bedrock["Amazon Bedrock"] Claude["Claude foundation models"] Candidates[(Candidate data)] Recruiter -->|"Role requirements + search"| FE FE --> API API --> Prompts Prompts --> Bedrock Bedrock --> Claude API --> Candidates Claude -->|"Semantic match scores"| API API -->|"Ranked recommendations"| FE
Summary
An AI-powered recruiting assistant that recommends candidates by best fit, using Amazon Bedrock and Claude foundation models for semantic matching.
What I worked on
- Integrated Amazon Bedrock into the recruiting application.
- Designed prompt workflows for semantic candidate–role matching.
- Leveraged Claude foundation models to deliver intelligent search and recommendations.
Results
- AI-assisted candidate recommendations based on best fit
- Semantic matching workflows powered by Claude on Bedrock
- Faster, more informed hiring decisions for recruiters