ATS — Event-Driven Applicant Tracking System

A production-style, distributed Applicant Tracking System built with Spring Boot microservices, Kafka, Redis, PostgreSQL, and real AI-powered resume screening using HuggingFace — fully containerized with Docker.

Designed as a distributed, event-driven system with asynchronous processing and AI-based candidate screening.

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What It Does

1. Candidate applies → Application is stored in PostgreSQL
2. Kafka event fired → Screening service processes it asynchronously
3. AI scores the resume → HuggingFace computes semantic similarity
4. Bonus logic applied → +10 if resume matches job title
5. Result published → Notification service sends email

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Architecture

Client
↓
API Gateway (JWT auth · port 8080)
↓
Application Service (port 8081)
→ generates JWT (/auth/login)
→ stores application in PostgreSQL
→ publishes to Kafka: application.submitted
↓
Kafka
↓
Screening Service (port 8082)
→ consumes application event
→ calls Job Service (Redis → PostgreSQL)
→ calls HuggingFace API (AI scoring)
→ publishes to Kafka: screening.completed
↓
Kafka
↓
Notification Service (port 8083)
→ sends result email via SMTP

![ATS Architecture](./ATS-architecture.drawio.png)

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Services

Service	Port	Responsibility
api-gateway	8080	Routing, entry point
application-service	8081	Authentication, application storage, Kafka producer
screening-service	8082	AI scoring, Kafka consumer/producer
notification-service	8083	Email notifications
job-service	8084	Job data, Redis caching, PostgreSQL

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Infrastructure

Component	Purpose
PostgreSQL	Application + job data storage
Apache Kafka	Event-driven communication
Redis	Job caching
HuggingFace API	AI scoring
SMTP (Gmail)	Email delivery

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Event Flow (Core Design)

application.submitted → produced by Application Service, consumed by Screening Service screening.completed → produced by Screening Service, consumed by Notification Service

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AI Screening Engine

Model used: sentence-transformers/all-MiniLM-L6-v2

Process:

Convert resume + job description → embeddings Compute similarity score (0–100) Add bonus if job title matches Cap score at 100

Score	Result
≥ 70	Passed
< 70	Rejected

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Redis Caching

First request  → PostgreSQL → cached in Redis  
Next requests  → served from Redis  

Used in Job Service via:

@Cacheable @CacheEvict

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Shared Module

Contains shared DTOs (e.g., ScreeningResultEvent) used across services.

Ensures:

consistent event structure reduced duplication better maintainability

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Automated Setup

Run everything with one command:

chmod +x setup.sh && ./setup.sh

The script:

builds shared module builds all services builds Docker images starts full system

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API Examples

Create Job

curl -X POST http://localhost:8084/jobs \
  -H "Content-Type: application/json" \
  -d '{"title":"Java Developer","description":"Spring Boot Kafka"}'

Submit Application

curl -X POST http://localhost:8081/apply \
  -H "Content-Type: application/json" \
  -d '{
    "candidateName":"John",
    "candidateEmail":"john@example.com",
    "resumeText":"Java Spring Boot Kafka developer",
    "jobId":1
  }'

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Security

JWT tokens generated in Application Service API Gateway routes authenticated requests

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Limitations

JWT validation not fully enforced in API Gateway filter No retry mechanism or DLQ for Kafka failures Event payload uses JSON instead of strict schema enforcement Job data fetch is synchronous (can introduce latency under load) Email delivery depends on external SMTP availability

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Future Enhancements

Dead-letter queue (DLQ) for Kafka Retry mechanisms Full JWT enforcement in gateway Resume parsing (PDF support) Recruiter dashboard Elasticsearch integration

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System Status

Component	Status
Microservices	Running
Kafka	Working
Redis	Working
PostgreSQL	Working
AI Scoring	Working
Email Notifications	Working
Docker Setup	Working

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Final Summary

This system demonstrates:

event-driven architecture microservices design AI integration caching strategies distributed system debugging containerized deployment

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License

MIT License