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Overview

The Applicant Search module enables companies to proactively search for qualified candidates based on various criteria including location, education, experience, and technical skills.

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Search Criteria

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Simplex Level (5.1.1 - 5.1.4)

Location

  • City OR Country (select one)
  • Single value per search

Education

  • Bachelor
  • Master
  • Doctorate

Work Experience

  • None
  • Any
  • Keyword search (case-insensitive)

Employment Types

Multiple selection allowed:
  • Full-time
  • Part-time
  • Internship
  • Contract

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Search Result Display

Basic Information Shown:
  • First Name
  • Last Name
  • Email
  • City
  • Country
  • Highest Education Degree
Detailed View: Click on applicant to see:
  • All basic information above
  • Education history
  • Work Experience details
  • Objective Summary

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Medium Level Features

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Full-Text Search (5.2.1)

Search across multiple fields simultaneously:
  • Work Experience
  • Objective Summary
  • Technical Skills
Example: Search for “software engineer python” finds applicants with these terms in any of the three fields.

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Technical Skills Filtering (5.2.2)

Uses OR logic for multiple skills
Skill Tag Search:
  • Filter by technical skill tags
  • Example: Search for “Kafka, Java, MongoDB”
  • Returns applicants with Kafka OR Java OR MongoDB
  • Not all three skills required
Common Skill Tags:
  • Languages: Python, Java, JavaScript, Go, Rust
  • Frameworks: React, Spring Boot, Django, Angular
  • Databases: MongoDB, PostgreSQL, MySQL, Redis
  • Tools: Docker, Kubernetes, Kafka, Git

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Performance Optimizations

  • Search results loaded incrementally
  • Improves performance with large datasets
  • Smooth scrolling experience
  • Reduces initial load time
Search page responsive to:
  • Desktop devices
  • Mobile devices
  • Tablet devices
  • Adaptive layout and controls

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Skill Tags Display (5.2.5)

Visual Skill Indicators

Each applicant result displays their skill tags for quick assessment:
  • Color-coded tags
  • Easy visual scanning
  • Quick skill matching

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Ultimo Level Features

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Database Sharding Optimization (5.3.1)

Requires database sharding implementation
Sharding Strategy:
  • Use Country as sharding key
  • Queries routed to relevant shard only
  • Significantly improves search performance
  • Reduces database load
Default Behavior:
  • System defaults to Vietnam location
  • Can be changed by user
  • Optimizes for primary user base

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Applicant Marking System (5.3.2)

  • Favorite
  • Warning
  • Mark promising candidates
  • Quick access for future reference
  • Indicated in search results
  • Company-specific favorites
Marking Contexts:
  • From profile view
  • From job application review
  • Visual indicators in search results

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Search Interface

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Basic Search Example

{
  "location": {
    "type": "COUNTRY",
    "value": "Vietnam"
  },
  "education": ["BACHELOR", "MASTER"],
  "workExperience": {
    "type": "KEYWORD",
    "value": "software engineer"
  },
  "employmentTypes": ["FULL_TIME", "INTERNSHIP"]
}

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Advanced Search with Skills

{
  "location": {
    "type": "CITY",
    "value": "Ho Chi Minh City"
  },
  "skills": ["React", "Spring Boot", "Docker"],
  "education": ["MASTER"],
  "fullTextSearch": "backend development microservices"
}

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Search Flow

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Implementation Guidelines

All text searches must be case-insensitive to ensure comprehensive results
Examples:
  • “Software Engineer” = “software engineer” = “SOFTWARE ENGINEER”
  • “python” = “Python” = “PYTHON”

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Performance Considerations

1

Index Fields

Create database indexes on searchable fields
2

Use FTS Engine

Implement full-text search engine (Elasticsearch, MongoDB Atlas Search)
3

Implement Pagination

Use cursor-based or offset pagination for lazy loading
4

Cache Results

Cache frequently accessed searches in Redis

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Data Integration

Applicant profile data must be consumed from Job Applicant subsystem API
Required API Calls:
  • GET /api/applicants/search
  • GET /api/applicants//profile
  • GET /api/applicants//documents