AI Job Matcher & Outreach Architecture

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This architecture describes how small machine learning models and a large language model (LLM) collaborate to deliver precise candidate-to-job matching and automated outreach within an Applicant Tracking or Recruitment CRM system.

1. Sensor Layer — Candidate and Job Profiling

  • Language Detector (LangID): Detects the candidate’s language to ensure multilingual matching and message tone.
  • Skill Tagger: Extracts key skills and maps them to standardized tags (e.g., “Python”, “Salesforce”, “Django”).
  • Geo & Seniority Models: Classify region and professional level to improve relevance.
  • Lead Scoring: Predicts the likelihood that a candidate will respond or fit the role.
  • PII Scrubber: Removes personal data before any text is sent to the LLM.

2. Orchestrator — Context Envelope Builder

The orchestrator merges all sensor outputs into a single Context Envelope: 

{
  "subject_id": "cand_9821",
  "facts": { "lang": "en", "geo": "BE", "skill_tags": ["python","django"] },
  "scores": { "lead": 0.87, "bot": 0.02, "seniority": 0.6 },
  "snippets": {
    "cv": "Senior Python developer with Django experience...",
    "job": "Backend engineer at OSG..."
  },
  "routing": { "task": "job_match_outreach_v1" }
}

The envelope guarantees structured, privacy-safe data for the next stage. It determines if the case goes to the LLM or follows a template fallback path.

3. LLM Layer — Reasoning & Message Generation

The LLM receives the context envelope and produces a JSON-structured decision: 

{
 "decision": "proceed",
 "reason": "High skill match & response probability",
 "message_draft": "Hi Alex, your Django experience aligns perfectly...",
 "next_action": "send",
 "tags": ["tech", "backend", "python"]
}

It ensures all messages remain on-brand, under 160 words, and localized based on language detection.

4. Tool Integration

  • faq_lookup() – Retrieves quick policy or job details.
  • calendar_slots() – Inserts three ready-to-book meeting times.
  • kb_search() – Fetches the top-3 text chunks from internal documentation.

5. Observability & Metrics

  • Precision@K: measures match accuracy.
  • Response Rate: measures candidate engagement.
  • Token Cost / Lead: monitors LLM efficiency.
  • Manual Override Rate: shows when humans need to step in.

6. Outcome & Business Value

The combination of local ML scoring and cloud-based LLM reasoning results in faster, cheaper, and more personalized outreach. Each candidate interaction becomes measurable, multilingual, and privacy-safe — optimizing both recruiter efficiency and candidate experience.

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