Agent HR - AI-Powered Candidate Screening & Interviewing on AWS

Executive Summary 

Customer, an AI and data foundry, transformed its recruitment services with "Agent HR," an AI-driven platform built on AWS. Facing manual processes, scalability, and data fragmentation challenges, Agent HR automates resume screening, provides real-time interview analysis with AI-powered interviewing, and predicts candidate success. Leveraging Amazon SageMaker, Transcribe, Comprehend, and Rekognition, the platform reduced hiring time by 40%, improved candidate quality by 25%, and achieved a 90% satisfaction rate among recruiters and candidates. With robust security and GDPR compliance, Agent HR positions customers as leaders in innovative HR solutions, delivering efficiency, fairness, and scalability. 

Customer Challenge 

Customer Information  

• Industry: Human Resources Technology 

• Location: Global 

Business Challenges 

The customer faced significant obstacles in modernizing its recruitment services: 

• Manual Processes: Recruitment relied on time-consuming manual tasks, leading to delays and inconsistent evaluations. 

• Lack of AI Integration: The absence of AI for resume screening, interview analysis, and candidate matching hindered efficiency. 

• Scalability Issues: Existing systems struggled to handle growing candidate volumes, impacting performance. 

• Data Silos: Fragmented data across systems prevented comprehensive candidate profiling. 

• Compliance Concerns: Strict GDPR and EEOC regulations demanded robust data security and fairness in hiring. 

• Business Goals: Customer aimed to reduce time-to-hire, enhance candidate quality, improve user experience, and ensure compliance while introducing innovative AI-driven interviewing. 

Technical Challenges 

• Legacy Systems: Outdated infrastructure lacked support for AI workloads, slowing development. 

• Integration Complexity: Connecting AI models with existing HR systems was technically challenging. 

• Real-time Processing: Delivering real-time interview analysis and AI-driven interview interactions required high-speed processing. 

• Data Management: Managing large volumes of unstructured data (resumes, interview transcripts, videos) demanded efficient solutions. 

• Security and Compliance: Encryption and access controls were critical to meet GDPR and EEOC standards. 

• Bias Mitigation: Ensuring fairness in AI-driven interviewing requires robust bias detection and mitigation. 

Partner Solution 

Solution Overview 

Customer partnered with AWS to develop Agent HR, an AI-powered platform streamlining recruitment. The solution leverages AWS’s machine learning, data analytics, serverless, and generative AI technologies to deliver: 

• AI-Driven Resume Screening: Parses resumes and matches candidates using natural language processing. 

• Real-time Interview Analysis: Transcribes and analyzes interviews for actionable insights. 

• AI-Based Interviewing: Uses AI-driven bots to conduct structured interviews and assess behavioral cues. 

• Predictive Candidate Scoring: Predicts candidate success with machine learning. 

• Inclusive Job Descriptions: Generates role-specific, inclusive job descriptions using Amazon Bedrock. 

• 360-Degree Candidate Profiles: Aggregates data for comprehensive insights. 

• Seamless Integration: Connects with HR systems via APIs. 

AWS Services Used 

Architecture Diagram

The architecture integrates AWS services for a scalable, secure solution: 

• Front-end: AWS Amplify hosts a responsive React-based web interface for recruiters.

• API Layer: Amazon API Gateway routes requests to AWS Lambda for secure processing.

Data Processing: 

• Amazon Transcribe converts interview audio to text. 

• Amazon Comprehend performs sentiment analysis and skill extraction. 

• Amazon Textract extracts structured data from resumes. 

• Amazon Bedrock generates inclusive job descriptions. 

• Machine Learning: Amazon SageMaker trains models for candidate scoring and bias detection. 

• Storage: Amazon Aurora stores structured data; Amazon S3 manages unstructured files. 

• Workflow: AWS Step Functions orchestrates recruitment processes. 

• Security: AWS KMS and Cognito ensure encryption and access control. 

• Monitoring: Amazon CloudWatch tracks performance and logs. 

Implementation Details 

The implementation followed an Agile methodology with DevOps practices, divided into four phases: 

Phase 1: Discovery and Planning (Weeks 1–2):

• Defined MVP focusing on resume screening, interview analysis, and AI-based interviewing. 

• Conducted workshops to align requirements with stakeholders. 

• Selected AWS services for scalability, real-time processing, and AI capabilities. 

Phase 2: Development (Weeks 3–8): 

• Built serverless backend with AWS Lambda and API Gateway. 

• Integrated Amazon Transcribe, Comprehend, Textract, Lex, and Rekognition for data processing and AI interviewing. 

• Developed ML models in SageMaker for scoring and bias detection. 

• Configured Amazon Aurora for structured data and S3 for unstructured files. 

Phase 3: Integration and Testing (Weeks 9–12): 

• Integrated Agent HR with Applicant Tracking Systems (ATS) and HRIS via REST APIs. 

• Used AWS Step Functions to orchestrate workflows. 

• Conducted load testing for scalability and automated unit, integration, and regression testing via AWS CodePipeline and CodeBuild. 

Phase 4: Deployment and Monitoring (Weeks 13–16):

• Deployed using AWS CloudFormation for infrastructure as code. 

• Configured CloudWatch for real-time monitoring and alerting. 

• Ensured GDPR and EEOC compliance with AWS KMS encryption, IAM access controls, and CloudTrail auditing. 

Security and Compliance Considerations: 

• Implemented AWS KMS for data encryption at rest and in transit. 

• Used Amazon SageMaker Clarify to detect and mitigate bias in AI interview models. 

• Configured AWS Cognito for secure authentication and role-based access. 

Timeline and Milestones: 

• Weeks 1–2: Planning and architecture design. 

• Weeks 3–8: Backend, AI/ML, and data pipelines development. 

• Weeks 9–12: System integration and performance testing. 

• Weeks 13–14: QA, security validation, and compliance audit. 

• Weeks 15–16: Production deployment and go-live. 

Innovation and Best Practices 

• Serverless Architecture: AWS Lambda and API Gateway reduced costs and enabled auto-scaling. 

• Real-time Processing: Amazon Kinesis ensured low-latency data analysis for interviews. 

• AI-Driven Interviewing: Amazon Lex and Rekognition enabled automated, structured interviews with behavioural analysis. 

• Bias Mitigation: SageMaker Clarify ensured fairness in AI models, aligning with EEOC guidelines. 

• Security and Compliance: AWS KMS and IAM ensured GDPR compliance. 

• Well-Architected Framework: Applied AWS best practices for reliability, security, and cost optimization. 

• DevOps Practices: Used AWS CodePipeline and CodeBuild for CI/CD automation.