# How the Six Defined Archetypes (FDE, SA, PM, LLMOps, Agentic, Transformation) Influence the Career-Ops Job Evaluation Process

> Discover how the six Career Ops archetypes (FDE, SA, PM, LLMOps, Agentic, Transformation) structure job evaluation by prioritizing CVs, applying templates, and calculating alignment scores.

- Repository: [Santiago Fernández de Valderrama/career-ops](https://github.com/santifer/career-ops)
- Tags: deep-dive
- Published: 2026-06-07

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**Career-Ops uses six role archetypes as a semantic classification layer that determines which CV proof points to prioritize, which narrative templates to apply, and how to calculate the final North-Star alignment score for every job opportunity.**

The `santifer/career-ops` repository employs an archetype-driven evaluation pipeline that begins at Step 0 in [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md). By categorizing every job description (JD) into one of six distinct archetypes—**AI Forward Deployed Engineer (FDE)**, **Solutions Architect (SA)**, **Technical AI Product Manager (PM)**, **LLMOps Engineer (LLMOps)**, **Agentic Automation (Agentic)**, and **AI Transformation Lead (Transformation)**—the system ensures that candidate recommendations are technically relevant and narratively coherent.

## Archetype Detection: The Entry Point

The evaluation process starts with archetype detection governed by keyword-based rules defined in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) (lines 78–85). The engine scans the JD for characteristic terms: “client-facing” or “fast delivery” trigger **FDE**, “architecture” or “integration” trigger **SA**, “roadmap” or “product manager” trigger **PM**, “observability” or “evals” trigger **LLMOps**, “agent” or “orchestration” trigger **Agentic**, and “transformation” or “adoption” trigger **Transformation**.

Once detected, the archetype is stored as the foundational classification that drives the remainder of the A–G scoring pipeline documented in [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) (lines 5–12).

## Three Pillars of Archetype-Driven Evaluation

The detected archetype influences the job evaluation through three interconnected mechanisms: proof-point prioritization, narrative framing, and North-Star alignment scoring.

### Proof-Point Prioritisation (Block B – Match with CV)

In Block B, the system maps JD requirements to the candidate’s CV by emphasizing archetype-specific proof points. According to [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) (lines 27–33), each archetype dictates a distinct prioritization schema:

- **FDE** prioritizes delivery speed and client-facing proof points.
- **SA** prioritizes system design and integrations.
- **PM** prioritizes product discovery and trade-offs.
- **LLMOps** prioritizes evals, observability, and pipelines.
- **Agentic** prioritizes multi-agent orchestration.
- **Transformation** prioritizes change-management and adoption metrics.

This selective emphasis ensures that the strongest, most relevant evidence surfaces first in the evaluation report.

### Narrative and Story Framing (Blocks C, E, and F)

The detected archetype selects the wording templates used for narrative construction. As specified in [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) (lines 76–82), the mapping drives language choices across three critical blocks:

1. **Block C (Sell Senior Without Lying)**: Tailors the “sell senior” plan to emphasize archetype-specific competencies—architectural decisions for SA, discovery processes for PM, or delivery speed for FDE.
2. **Block E (Customisation Plan)**: Generates CV and LinkedIn edit lists that highlight achievements aligned with the archetype’s expectations.
3. **Block F (STAR+R Stories)**: Generates interview stories framed around the archetype’s core proof points, ensuring the candidate showcases the most compelling experiences during technical and behavioral interviews.

### North-Star Alignment (Score A–F)

The global “North Star alignment” dimension—defined in Section 31–34 of [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md)—compares the detected archetype against the user-defined target archetypes stored in [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md). A close match between the JD’s detected archetype and the candidate’s preferred archetype lifts the overall score, while a mismatch reduces it, ensuring that recommendations align with long-term career trajectory goals.

## Implementation in Code

The following Node.js snippet illustrates the core logic used within the Career-Ops pipeline to select proof-point priorities based on the detected archetype:

```javascript
// src/archetype.js – core logic (illustrative)
import { readFileSync } from 'fs';
import profile from '../modes/_profile.md';

const ARCHETYPE_RULES = {
  FDE:   { proofPoints: ['delivery speed', 'client-facing'] },
  SA:    { proofPoints: ['system design', 'integration'] },
  PM:    { proofPoints: ['product discovery', 'metrics'] },
  LLMOps:{ proofPoints: ['evals', 'observability', 'pipelines'] },
  Agentic:{ proofPoints: ['multi-agent', 'orchestration'] },
  Transformation:{ proofPoints: ['change-management', 'adoption'] },
};

function detectArchetype(jdText) {
  const lower = jdText.toLowerCase();
  if (lower.includes('client-facing') || lower.includes('fast delivery')) return 'FDE';
  if (lower.includes('architecture') || lower.includes('integration')) return 'SA';
  if (lower.includes('roadmap') || lower.includes('product manager')) return 'PM';
  if (lower.includes('observability') || lower.includes('evals')) return 'LLMOps';
  if (lower.includes('agent') || lower.includes('orchestration')) return 'Agentic';
  if (lower.includes('transformation') || lower.includes('adoption')) return 'Transformation';
  return 'UNKNOWN';
}

// Example usage within the evaluation pipeline
const jd = readFileSync('job.txt', 'utf8');
const archetype = detectArchetype(jd);
const proofPoints = ARCHETYPE_RULES[archetype].proofPoints;

// Block B – map JD requirements to CV lines using the selected proof points
const blockB = mapRequirementsToCV(jd, proofPoints, 'cv.md');

```

In production, this logic is orchestrated through [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) (Step 0–4) and [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) (Archetype detection table), ensuring that the classification propagates through Blocks B, C, E, and F before final scoring.

## Summary

- **Archetype classification occurs at Step 0** in [`modes/oferta.md`](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) and acts as the foundational lens for all subsequent evaluation steps.
- **Proof-point prioritization** in Block B uses archetype-specific criteria to map JD requirements to the strongest CV evidence.
- **Narrative framing** in Blocks C, E, and F applies archetype-aligned templates for seniority positioning, CV customization, and interview storytelling.
- **North-Star alignment** compares detected archetypes against user preferences in [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md) to calculate the final suitability score.
- **Keyword-based detection** in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) (lines 78–85) enables automatic classification without manual tagging.

## Frequently Asked Questions

### How does Career-Ops detect which archetype applies to a job description?

The system uses a keyword-based rule set defined in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md) (lines 78–85) that scans the JD text for characteristic terms. For example, occurrences of “client-facing” or “fast delivery” classify the role as **FDE**, while “orchestration” or “multi-agent” classify it as **Agentic**. This detection runs automatically at Step 0 of the evaluation pipeline before any scoring occurs.

### Can I target multiple archetypes in my Career-Ops profile?

Yes. The [`modes/_profile.md`](https://github.com/santifer/career-ops/blob/main/modes/_profile.md) file stores your target archetypes and custom proof-point mappings. The North-Star alignment score (Section 31–34 of [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md)) compares the JD’s detected archetype against these preferences. A close match increases your overall alignment score, while a mismatch lowers it, helping you filter for roles that fit your career trajectory.

### What happens if a job description fits multiple archetypes?

The detection logic in [`src/archetype.js`](https://github.com/santifer/career-ops/blob/main/src/archetype.js) (and the underlying rules in [`modes/_shared.md`](https://github.com/santifer/career-ops/blob/main/modes/_shared.md)) follows a priority-based sequence. The first matching archetype is selected based on keyword precedence. If no keywords match, the system returns `UNKNOWN`, and the evaluation pipeline defaults to generic proof-point prioritization until manual classification is provided.

### How do the archetypes affect the final PDF or CV output?

The detected archetype influences the `templates/` directory (referenced in [`modes/pdf.md`](https://github.com/santifer/career-ops/blob/main/modes/pdf.md), lines 6–12) to adapt visual emphasis and section ordering. For instance, an **LLMOps** detection will surface “Evaluations & Observability” sections prominently, while a **Transformation** detection will prioritize “Change Management” and “Adoption Metrics” in the generated CV layout.