Job Architecture

The Job Architecture capability uses advanced AI to extract structured information from unstructured job descriptions and candidate profiles. It transforms free-text into organized data about roles, responsibilities, skills, and requirements that power intelligent talent decisions.

Overview

Job Architecture extraction is a cornerstone of Beamery's talent intelligence platform. It takes messy, unstructured data and produces clean, structured outputs that can be used for:

Skills-based hiring - Understand true skill requirements beyond keywords
Job design - Analyze role compositions and optimize structures
Workforce planning - Map capabilities across the organization
Career pathing - Identify skill progressions and mobility paths

What Gets Extracted

From Job Descriptions:

Core responsibilities and tasks
Required and preferred skills
Experience requirements
Seniority level
Industry context
Team structure

From Candidate Profiles:

Current and past roles
Demonstrated skills
Years of experience
Industry expertise
Career progression
Notable achievements

Job Architecture Model

The extracted job architecture follows a structured model designed for maximum utility.

Core Components

Name
role
Type
object
Description
Standardized role information including title, seniority, and category
Name
tasks
Type
array
Description
Breakdown of key responsibilities with metadata
Name
skills
Type
object
Description
Categorized skills with proficiency levels and requirements
Name
requirements
Type
object
Description
Experience, education, and certification requirements
Name
context
Type
object
Description
Industry, team, and organizational context

Task Analysis

Each extracted task includes:

Task structure

{
  "id": "task_identifier",
  "description": "Clear task description",
  "frequency": "daily|weekly|monthly|quarterly",
  "importance": "critical|high|medium|low",
  "effort": "high|medium|low",
  "automation_potential": "high|medium|low",
  "required_skills": ["skill_id1", "skill_id2"],
  "task_category": "analysis|development|management"
}

Task Metadata Helps With:

Workload estimation
Role optimization
Automation opportunities
Training needs analysis
Team structure design

Skill Proficiency Mapping

Proficiency Levels:

Beginner - Basic understanding
Intermediate - Working knowledge
Experienced - Independent work
Advanced - Expert level
Expert - Thought leader

Requirement Types:

Required - Must have for the role
Preferred - Nice to have
Inferred - Implied by context

Skill with metadata

{
  "id": "skill_kubernetes",
  "name": "Kubernetes",
  "proficiency": "experienced",
  "requirement": "preferred",
  "category": "infrastructure",
  "broader_skill": "skill_container_orchestration",
  "related_skills": [
    "skill_docker",
    "skill_helm"
  ],
  "years_required": 2,
  "context": "production_deployment"
}

Extraction Examples

Software Engineering Role

Input: Full-stack developer JD

{
  "title": "Full-Stack Developer",
  "description": "Join our team as a Full-Stack Developer working on our SaaS platform. You'll build features end-to-end using React and Node.js. Experience with PostgreSQL and Redis required. You should be comfortable with CI/CD, testing, and agile methodologies."
}

Extracted architecture

{
  "role": {
    "title": "Full-Stack Developer",
    "category": "engineering",
    "seniority": "mid-level"
  },
  "skills": {
    "required": [
      {"name": "React", "proficiency": "experienced"},
      {"name": "Node.js", "proficiency": "experienced"},
      {"name": "PostgreSQL", "proficiency": "intermediate"},
      {"name": "Redis", "proficiency": "intermediate"}
    ],
    "inferred": [
      {"name": "JavaScript", "confidence": 0.95},
      {"name": "REST APIs", "confidence": 0.88},
      {"name": "Git", "confidence": 0.85}
    ]
  }
}

Executive Profile

Input: VP resume

{
  "summary": "VP of Engineering with 15+ years experience. Led teams of 50+ engineers at scale-ups and Fortune 500 companies. Transformed engineering culture at TechCorp, reducing deployment time by 80%. Expert in cloud migration, DevOps transformation, and building high-performance teams."
}

Extracted profile

{
  "role": {
    "title": "VP of Engineering",
    "seniority": "executive",
    "years_experience": 15
  },
  "skills": {
    "technical": [
      {"name": "Cloud Architecture", "proficiency": "expert"},
      {"name": "DevOps", "proficiency": "expert"}
    ],
    "leadership": [
      {"name": "Team Building", "proficiency": "expert"},
      {"name": "Engineering Management", "proficiency": "expert"},
      {"name": "Strategic Planning", "proficiency": "advanced"}
    ]
  },
  "achievements": [
    "Reduced deployment time by 80%",
    "Led teams of 50+ engineers"
  ]
}

Best Practices

Input Quality

DO:

Provide complete job descriptions
Include specific technologies and tools
Mention team structure and reporting
Specify experience requirements
Include industry context

DON'T:

Use generic job templates
Rely only on job titles
Include discriminatory language
Mix multiple roles in one description
Use excessive jargon

Data Enrichment

Use extracted architecture to enrich your talent data:

Standardize job titles across the organization
Map skill requirements to create capability models
Identify skill gaps in teams and departments
Build career paths based on skill progressions
Optimize job designs by analyzing task distributions

Continuous Improvement

The extraction models are continuously updated based on:

Latest market trends
New technologies and skills
Customer feedback
Taxonomy updates

Limitations

Maximum 10 profiles per request
Text should be under 10,000 characters per profile
Some languages may have reduced accuracy
Highly technical or niche roles may need calibration