Development of a Microservices-Based AI-Driven Talent Acquisition Platform Enhancing Data Processing and Matching Accuracy

The client operates a web-based talent acquisition platform built on outdated architecture, resulting in poor scalability, slow data processing, and limited AI integration for candidate matching. As the client aims to serve a growing customer base and implement advanced data mining, existing systems impair business growth and candidate selection accuracy, necessitating a comprehensive upgrade to a flexible, high-performance architecture.

A mid to large-scale HR and recruiting technology provider specializing in talent acquisition solutions utilizing AI and machine learning.

• Achieve a 50% increase in data processing speed through microservices implementation.
• Reduce system downtime by 65% by improving fault isolation and resilience.
• Enhance code maintainability by 35% via code refactoring and modernization.
• Optimize 80% of server requests for faster response times.
• Implement new AI/ML functionalities to enable instant candidate matching based on specific parameters.
• Migrate existing codebase from legacy Python 2 to Python 3 for better unification and support.
• Develop scalable microservices architecture to support seamless feature expansion and stability.

• Modular microservices to facilitate independent deployment and scalability.
• Advanced candidate matching algorithm using AI and machine learning for high accuracy and speed.
• Integration of a search engine platform (e.g., Elasticsearch) to enable efficient data analysis and retrieval.
• Automated data processing pipeline with significant speed improvements.
• Real-time candidate shortlist generation based on customizable parameters.
• Code modernization from legacy Python to current Python versions.
• Automated testing suites to ensure system stability and reliability.

• AI/ML frameworks and libraries for candidate matching
• External data sources/databases for talent pool enrichment
• Existing HR systems or job boards for data synchronization
• Analytics and monitoring tools for system health and performance

• System should support at least double the current data volume with minimal latency
• Aim for 50% faster data processing times
• Achieve 65% reduction in system downtime through fault isolation
• Codebase scalability to support rapid feature deployment
• Security measures compliant with data protection standards (e.g., GDPR)

The upgraded system is expected to improve data processing speeds by at least 50%, significantly reduce system downtime by 65%, and enhance code maintainability by 35%. The new AI-powered matching algorithm will deliver high-accuracy candidate recommendations swiftly, supporting increased customer acquisition and retention, thus enabling the client to serve more users efficiently and effectively.