Candidate Positioning

When to Use This Skill

Use this skill as Phase -1 — the very first step — before JD analysis, before bullet generation, before anything. This skill answers the most important question:

"What kind of candidate are you?"

Trigger phrases: "position me for", "what role should I target", "generate resume", "create resume variant", any resume generation request

This skill runs once per candidate profile, then the output is reused across all JD-specific tailoring.

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Core Principle

A recruiter must identify your role identity within 6 seconds. A resume that says "I know 30 technologies" communicates nothing. A resume that says "I'm an AI/ML engineer who builds production ML systems with Python and FastAPI" communicates everything.

Positioning happens BEFORE keyword optimization. You cannot optimize what you haven't positioned.

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Step 1: Profile Analysis

Analyze the candidate's full profile (INPUT 2) across these dimensions:

Experience Signals

Signal	Weight	What to Extract
Job titles	High	Direct role identity (e.g., "AI Intern" → AI/ML signal)
Technologies used at work	High	Production-level tech stack
Project domains	Medium	What problems they solve (ML, web apps, CLI tools, infra)
Certifications	Medium	Industry validation direction (Azure AI, AWS, etc.)
Education focus	Low	Coursework alignment

Project Technology Distribution

Count how many projects use each technology category:

• AI/ML: ML models, LLMs, RAG, agents, vector databases, anomaly detection
• Backend: REST APIs, FastAPI, Express, databases, authentication
• Frontend: React, Next.js, UI components, responsive design
• DevOps/Cloud: Docker, CI/CD, cloud deployment, infrastructure
• Data Engineering: Pipelines, ETL, data processing, streaming
• CLI/Tooling: Command-line tools, developer experience, automation

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Step 2: Role Family Classification

Based on the profile analysis, classify into ONE primary and ONE secondary role family.

Available Role Families

Role Family	Key Signals
SDE / Software Engineer	Strong DSA, multiple languages, backend/API work, system design, production software
AI/ML Engineer	ML models, LLMs, RAG, AI agents, data pipelines, Python-heavy, research papers
Full Stack Developer	React/frontend + Node/backend, MERN/MEAN, responsive UI, authentication, RBAC
DevOps / Cloud Engineer	Docker, Kubernetes, CI/CD, cloud certifications, infrastructure, monitoring
Data Engineer	ETL pipelines, Spark, Kafka, data warehouses, SQL-heavy, streaming

Classification Rules

1. Primary role = the role family with the most evidence (projects + experience + certifications combined)
2. Secondary role = the next strongest, used for variant generation
3. If tied: Prefer the role family that matches the candidate's most recent experience
4. Never classify as 3+ role families — that's the "I know everything" anti-pattern

Classification Output

## Role Family Classification

**Primary:** AI/ML Engineer
**Evidence:** 3/6 projects are ML/AI, AI internship, Azure AI + Oracle AI certifications, LangGraph/CrewAI/RAG experience
**Secondary:** Software Engineer (Backend)
**Evidence:** FastAPI, REST APIs, Docker, Node.js/Express, database design

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Step 3: Core Technical Identity

Select 8-12 core technologies that define this candidate for the primary role family. These are the technologies that MUST appear prominently in the resume.

Selection Rules

1. Include: Technologies the candidate has used in ≥2 projects or ≥1 work experience
2. Include: Technologies that appear in >50% of JDs for the primary role family
3. Exclude: Technologies used in only 1 tutorial-level project
4. Exclude: Technologies that dilute the role identity (e.g., CrewAI for SDE roles)
5. Maximum 12 — fewer is stronger

Core Identity by Role Family (Examples)

For SDE:

Core: Python, C++, JavaScript, TypeScript, SQL, FastAPI, Node.js, Express, React, Docker, Git
Remove/Minimize: CrewAI, LangGraph, RAG, Neo4j, Azure OpenAI, FAISS, LangChain

For AI/ML Engineer:

Core: Python, ML, Scikit-learn, LLMs, RAG, LangGraph, FastAPI, FAISS, Elasticsearch, Docker, Git
Remove/Minimize: Next.js, Tailwind CSS, Bootstrap, Prisma ORM, GraphQL

For Full Stack:

Core: React, Next.js, Node.js, Express, TypeScript, MongoDB, MySQL, REST APIs, Docker, Git, Tailwind CSS
Remove/Minimize: CrewAI, LangGraph, FAISS, Elasticsearch, Neo4j, Isolation Forest

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Step 4: Project Priority Ranking

Rank ALL candidate projects by relevance to the primary role family. Output a priority list with selection recommendations.

Ranking Criteria

Factor	Weight
Technology overlap with core identity	40%
Problem domain relevance	25%
Architecture complexity	20%
Deployment/demo evidence	15%

Output Format

## Project Priority for [Role Family]

### Include on Resume (Top 2-3)
1. **[Project Name]** — [Why it fits: covers X core technologies, demonstrates Y]
2. **[Project Name]** — [Why it fits]
3. **[Project Name]** — [Why it fits, if space permits]

### Keep in Portfolio Only
4. **[Project Name]** — [Good project but doesn't fit this role variant]
5. **[Project Name]** — [Domain mismatch for this role]

### Recommendation
Lead with [Project 1] — it covers [N] critical technologies and demonstrates [problem-solving type].

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Step 5: Skill Section Strategy

Determine the Technical Skills section layout for the resume.

Rules

1. Maximum 5 categories in Technical Skills
2. Maximum 15 total items across all categories for fresher resumes
3. Order categories by JD relevance (most relevant first)
4. Order items within categories by proficiency and relevance
5. Remove technologies that:
   • Are not in the core identity AND not in the JD
   • Were used in only one tutorial-level project
   • Dilute the primary role signal

Category Templates by Role

SDE:

Languages: Python, C++, JavaScript, TypeScript, SQL
Backend: FastAPI, Node.js, Express.js, REST APIs
Frontend: React.js, HTML5, CSS3
Tools: Docker, Git, GitHub, CI/CD
Databases: MongoDB, MySQL, PostgreSQL

AI/ML:

Languages: Python, SQL, JavaScript
AI/ML: Scikit-learn, LLMs, RAG, LangGraph, CrewAI, FAISS
Backend: FastAPI, REST APIs, Elasticsearch
Cloud: Azure AI, AWS, Docker, CI/CD
Databases: MongoDB, PostgreSQL, Neo4j

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Step 6: Certification Selection

Select 2-3 certifications most relevant to the primary role family.

Rules

1. Maximum 3 certifications on a fresher resume
2. Relevance > quantity — 2 relevant certs beat 5 irrelevant ones
3. Order by: Role relevance → Issuer prestige → Recency
4. Certifications not selected still exist on LinkedIn — they're not deleted, just not on THIS resume

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Full Output: Candidate Position Brief

This brief is consumed by ALL downstream stages (JD Analyzer, Bullet Generator, Humanizer, Critic).

## Candidate Position Brief

**Candidate:** [Name]
**Primary Role:** [Role Family]
**Secondary Role:** [Role Family]
**Generated:** [Date]

### Core Technical Identity (8-12 technologies)
[Ordered list]

### Project Priority
1. [Project] — Include
2. [Project] — Include
3. [Project] — Include if space
4. [Project] — Portfolio only
...

### Skill Section Layout
[Category: items] format

### Certifications to Include
1. [Cert]
2. [Cert]

### Technologies to Remove/Minimize
[List with reasons]

### Candidate Positioning Statement
(1-2 sentence identity used internally, NOT placed on resume)
"[Name] is an AI/ML-focused backend engineer with internship experience building
ML systems and multiple deployed AI projects. Strongest in Python, FastAPI,
and ML pipeline development."

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Rules

1. This skill runs BEFORE the JD Intelligence Analyzer. The JD Analyzer then uses this brief to refine (not override) the positioning.
2. One primary role per resume. If the JD clearly fits the secondary role, switch — but never try to be both simultaneously.
3. The brief is a filter, not a fabrication. It selects and orders true experience. It never adds skills or projects the candidate doesn't have.
4. Update the brief when the candidate profile changes (new project, new certification, new experience). Don't regenerate for every JD.
5. For experienced candidates (3+ years): This skill is less critical because work experience naturally positions. For freshers, it's THE most important step.