# The Six Career-Ops Archetypes and Their Key JD Signals

> Discover the six career-ops archetypes like AI Platform and Agentic Automation. Learn their key job description signals to tailor your candidate narrative and align with role requirements.

- Repository: [Santiago Fernández de Valderrama/career-ops](https://github.com/santifer/career-ops)
- Tags: deep-dive
- Published: 2026-08-22

---

**Career-Ops defines six distinct archetypes—AI Platform/LLMOps, Agentic/Automation, Technical AI PM, AI Solutions Architect, AI Forward Deployed, and AI Transformation—each triggered by specific keyword signals in job descriptions to align candidate narratives with role requirements.**

The `santifer/career-ops` repository implements an intelligent classification system that categorizes AI roles into six core **career-ops archetypes**. This framework, defined in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md), automatically analyzes job descriptions for specific lexical markers to determine role fit and drive downstream application strategies.

## Understanding the Six Career-Ops Archetypes

The archetype detection engine in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) scans incoming JDs for distinctive keyword patterns. Each category represents a fundamental AI career trajectory with specific technical and functional markers.

### AI Platform / LLMOps

This archetype focuses on infrastructure reliability and large language model operations. Key signals include: **"observability"**, **"evals"**, **"pipelines"**, **"monitoring"**, and **"reliability"**.

### Agentic / Automation

Roles in this category emphasize autonomous systems and human-in-the-loop workflows. Detection relies on keywords: **"agent"**, **"HITL"**, **"orchestration"**, **"workflow"**, and **"multi-agent"**.

### Technical AI PM

Product-oriented positions that bridge engineering and business strategy. Identifying terms include: **"PRD"**, **"roadmap"**, **"discovery"**, **"stakeholder"**, and **"product manager"**.

### AI Solutions Architect

Enterprise-focused design and integration roles. The system flags: **"architecture"**, **"enterprise"**, **"integration"**, **"design"**, and **"systems"**.

### AI Forward Deployed

Client-facing implementation and rapid prototyping positions. Signals comprise: **"client-facing"**, **"deploy"**, **"prototype"**, **"fast delivery"**, and **"field"**.

### AI Transformation

Change management and organizational adoption specialists. Key indicators are: **"change management"**, **"adoption"**, **"enablement"**, and **"transformation"**.

## How Archetype Detection Works in career-ops

The classification logic resides in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) and operates during the JD evaluation flow defined in [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md). When processing a posting, the system performs lexical pattern matching against the keyword lists for each archetype.

If the keyword analysis suggests attributes from multiple categories, the system classifies the position as a **hybrid role** and reports the two closest archetype matches. This dual-tagging captures complex positions that span multiple disciplines, ensuring the candidate narrative addresses both functional areas.

## Configuring Your Target Archetypes

Define your target archetypes in [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md) (never edit the canonical definitions in [`_shared.md`](https://github.com/santifer/career-ops/blob/main/_shared.md)). This configuration specifies which archetypes align with your experience level and career direction:

```yaml

# modes/_profile.md (excerpt)

archetypes:
  - name: AI Platform / LLMOps
    level: senior
    fit: direct
  - name: Agentic / Automation
    level: staff
    fit: adjacent

```

## Evaluating Job Descriptions Against Archetypes

Run the `oferta` mode to analyze a specific posting and view the detected archetype classification:

```bash

# Evaluate a posting (the mode will print the detected archetype)

node oferta.mjs https://example.com/job/12345

# → Detected archetype: AI Platform / LLMOps

```

For batch processing pipelines, implement custom filtering logic that leverages the detection system to pre-screen URLs:

```javascript
// batch/pipeline-filter.mjs
import { readFileSync } from 'fs';
const profile = readFileSync('modes/_profile.md', 'utf8');
const targetArchetypes = ['AI Platform / LLMOps', 'Agentic / Automation'];
// ... after fetching each JD, run the detection logic and keep only those matching targetArchetypes

```

## Impact on Scoring and Pipeline Analytics

The detected archetype influences three critical pipeline functions according to the source code implementation:

- **Narrative Alignment**: [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md) uses the archetype to frame your experience narrative toward the role's specific focus area.
- **Proof-Point Selection**: The system selects relevant achievements from [`cv.md`](https://github.com/santifer/career-ops/blob/main/cv.md) and [`article-digest.md`](https://github.com/santifer/career-ops/blob/main/article-digest.md) based on archetype relevance.
- **North-Star Scoring**: In [`modes/triage.md`](https://github.com/santifer/career-ops/blob/main/modes/triage.md), archetype alignment carries a **30% weight** in the quick triage score, directly impacting the **North-Star alignment** dimension of the overall evaluation.

Additionally, [`modes/patterns.md`](https://github.com/santifer/career-ops/blob/main/modes/patterns.md) aggregates success and failure metrics per archetype across your tracker, enabling data-driven optimization of application strategies for specific role categories.

## Summary

- Career-Ops defines six archetypes: AI Platform/LLMOps, Agentic/Automation, Technical AI PM, AI Solutions Architect, AI Forward Deployed, and AI Transformation.
- Detection relies on specific keyword signals defined in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md), with hybrid roles reporting the two closest matches.
- Configure target archetypes in [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md) with specific seniority levels (e.g., senior, staff) and fit designations (direct or adjacent).
- The archetype drives narrative framing in the profile, proof-point selection from source documents, and contributes 30% weight to the triage scoring algorithm.
- Analytics tracking in [`modes/patterns.md`](https://github.com/santifer/career-ops/blob/main/modes/patterns.md) monitors performance metrics across archetype categories for strategic optimization.

## Frequently Asked Questions

### How does career-ops determine which archetype applies to a job description?

The system scans the JD text for predefined keyword signals listed in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md). When keywords associated with a specific archetype—such as "observability" for AI Platform/LLMOps or "HITL" for Agentic/Automation—are detected above the threshold frequency, the role is tagged with that classification.

### Can a role match more than one career-ops archetype?

Yes. When keyword patterns suggest attributes from multiple categories, the system classifies the position as a hybrid and reports the two closest archetype matches. This captures dual-focus roles such as Technical AI PM positions that also require Solutions Architecture skills.

### Where do I configure my preferred archetypes in career-ops?

Define your target archetypes in [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md), specifying the archetype name, your experience level, and fit type. Never modify the canonical detection table in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md), as that file contains the global keyword definitions used by the system.

### How does archetype detection affect my overall application score?

According to the scoring system implemented in [`modes/triage.md`](https://github.com/santifer/career-ops/blob/main/modes/triage.md), archetype alignment contributes **30% of the total weight** in the quick triage score. It also influences the North-Star alignment dimension and determines which specific proof points from your [`cv.md`](https://github.com/santifer/career-ops/blob/main/cv.md) and [`article-digest.md`](https://github.com/santifer/career-ops/blob/main/article-digest.md) files are selected for emphasis in the generated application materials.