Prompt Engineer Job Description Sample

Prompt Engineer / LLM Engineer Job Summary

The Prompt Engineer / LLM Engineer designs and improves interactions between users and large language models. This role focuses on creating effective prompts, refining model outputs, and ensuring responses are accurate, relevant, and safe. The engineer helps teams use language models to automate tasks, generate content, and support decision-making at scale.

Prompt Engineer / LLM Engineer Overview

The Prompt Engineer / LLM Engineer works closely with product, engineering, and data teams to build reliable language model solutions. The role involves understanding user intent, structuring prompts, and testing responses across real-world scenarios. The engineer ensures that language model behavior aligns with product goals, quality standards, and responsible usage guidelines.

Prompt Engineer / LLM Engineer Responsibilities and Duties

• Designs and refines prompts by testing different structures, instructions, and examples
• Evaluates model responses by measuring accuracy, relevance, and consistency
• Improves output quality by adjusting prompts, parameters, and context handling
• Builds prompt frameworks by creating reusable templates and guidelines
• Collaborates with product and engineering teams by translating use cases into prompt strategies
• Integrates language models by supporting application programming interfaces and workflows
• Tests edge cases by identifying failure scenarios and reducing incorrect or unsafe outputs
• Documents prompt strategies by explaining logic, constraints, and expected outcomes

Prompt Engineer / LLM Engineer Qualifications and Skills

Must-Haves

• Strong understanding of how large language models generate text
• Experience writing and testing prompts for conversational and task-based use cases
• Proficiency in Python or similar languages for prompt testing and automation
• Ability to evaluate model outputs using clear quality and performance criteria
• Strong written communication skills for creating precise and unambiguous instructions

Nice-to-Haves

• Experience working with popular large language model platforms and tools
• Knowledge of fine-tuning concepts, embeddings, and retrieval-based methods
• Familiarity with safety, bias, and responsible use practices for language models
• Experience collaborating in agile or cross-functional teams
• Understanding of user experience principles related to conversational interfaces