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Modernization of HR Talent Acquisition Platform Using Microservices and AI/ML Enhancements

light-it.net

Human Resources

Information technology

Legacy System Limitations Hindering Scalability and Performance

The client's monolithic architecture and outdated Python 2 codebase caused poor scalability, slow data processing, frequent downtime, and technical debt. These limitations prevented efficient AI/ML-based talent matching and hindered business growth due to inability to handle increasing data volumes and real-time processing demands.

About the Client

A Netherlands-based HR technology company specializing in AI-driven talent acquisition solutions, including talent mobility platforms and recruiting ecosystems.

Platform Modernization Goals

Implement microservices architecture for improved scalability and fault isolation
Accelerate data processing speed by 50% through architectural optimization
Reduce system downtime by 65% via modular design
Enhance code maintainability by 35% through Python 3 migration
Optimize 80% of server requests for real-time talent matching

Core System Capabilities

Microservices-based architecture with independent deployment modules
AI/ML-driven candidate-job matching algorithm with instant results
Python 3 codebase migration and unification
Elasticsearch integration for fast data indexing and search
Automated testing framework for continuous integration

Technology Stack Requirements

Django

Python 3

ElasticSearch

PostgreSQL

Cassandra

RabbitMQ

System Integration Needs

Existing HR databases
AI/ML frameworks
Third-party recruitment APIs

Operational Requirements

Horizontal scalability to handle 10x user growth
99.95% system availability through fault isolation
Response time under 200ms for matching queries
Role-based access control for data security
Cross-platform compatibility with mobile and web

Expected Business Outcomes

The modernized platform will enable 50% faster data processing, 65% reduced downtime, and 35% improved maintenance efficiency. This will result in enhanced AI-driven talent matching accuracy, 80% optimized server requests, and support for rapid business expansion while maintaining system stability under increased loads.