Skill Profile

LLM Prompt Engineering

This skill defines expectations across roles and levels.

Machine Learning & AI LLM & Generative AI

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 10 optional

Machine Learning & AI

LLM & Generative AI

2/22/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Data Scientist		Creates basic prompts for LLM: structured queries for data analysis, code generation, information extraction. Understands zero-shot and few-shot prompting principles. Iteratively improves prompts based on model response quality.
LLM Engineer		Knows basic prompt engineering techniques: zero-shot, few-shot, chain-of-thought. Creates simple prompts for classification and text generation tasks, tests variations under mentor guidance.

Role	Required	Description
Data Scientist		Applies advanced prompting techniques for data science tasks: chain-of-thought, self-consistency, ReAct. Designs prompt pipelines for analytics automation. Evaluates prompt quality through systematic benchmarking on test sets.
LLM Engineer		Independently develops complex prompt templates: structured output, tool use, multi-step reasoning. Conducts systematic prompt optimization with A/B testing and quality metrics.

Role	Required	Description
Data Scientist		Designs production prompt engineering systems for scalable LLM applications. Applies prompt optimization, automatic prompt tuning. Builds prompt testing frameworks with regression testing and quality monitoring for production.
LLM Engineer		Designs production prompt engineering systems: prompt versioning, automated optimization, meta-prompting. Implements dynamic prompt construction based on context and user intent.

Role	Required	Description
Data Scientist		Defines prompt engineering strategy for the data science team. Establishes shared prompt library and best practices. Coordinates prompt development workflow with version control and collaborative review.
LLM Engineer		Defines prompt engineering standards for the LLM team. Establishes prompt libraries, prompt testing guidelines, review processes. Coordinates prompt optimization for production systems.

Role	Required	Description
Data Scientist		Shapes prompt engineering strategy at organizational level. Defines governance model for prompts: security, cost optimization, quality standards. Evaluates prompting paradigm evolution and prepares the organization for changes.
LLM Engineer		Shapes enterprise prompt engineering strategy. Defines approaches to centralized prompt management, automated prompt optimization, and prompt quality at organizational scale.

Junior 2 requirements

Data Scientist

Creates basic prompts for LLM: structured queries for data analysis, code generation, information extraction. Understands zero-shot and few-shot prompting principles. Iteratively improves prompts based on model response quality.
LLM Engineer

Knows basic prompt engineering techniques: zero-shot, few-shot, chain-of-thought. Creates simple prompts for classification and text generation tasks, tests variations under mentor guidance.

Middle 2 requirements

Data Scientist

Applies advanced prompting techniques for data science tasks: chain-of-thought, self-consistency, ReAct. Designs prompt pipelines for analytics automation. Evaluates prompt quality through systematic benchmarking on test sets.
LLM Engineer

Independently develops complex prompt templates: structured output, tool use, multi-step reasoning. Conducts systematic prompt optimization with A/B testing and quality metrics.

Senior 2 requirements

Data Scientist

Designs production prompt engineering systems for scalable LLM applications. Applies prompt optimization, automatic prompt tuning. Builds prompt testing frameworks with regression testing and quality monitoring for production.
LLM Engineer

Designs production prompt engineering systems: prompt versioning, automated optimization, meta-prompting. Implements dynamic prompt construction based on context and user intent.

Lead / Staff 2 requirements

Data Scientist

Defines prompt engineering strategy for the data science team. Establishes shared prompt library and best practices. Coordinates prompt development workflow with version control and collaborative review.
LLM Engineer

Defines prompt engineering standards for the LLM team. Establishes prompt libraries, prompt testing guidelines, review processes. Coordinates prompt optimization for production systems.

Principal 2 requirements

Data Scientist

Shapes prompt engineering strategy at organizational level. Defines governance model for prompts: security, cost optimization, quality standards. Evaluates prompting paradigm evolution and prepares the organization for changes.
LLM Engineer

Shapes enterprise prompt engineering strategy. Defines approaches to centralized prompt management, automated prompt optimization, and prompt quality at organizational scale.

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