For a mid-level Prompt Engineer in technology, a resume must clearly showcase contributions that directly impact AI model performance and application effectiveness. Hiring managers seek evidence of successful prompt optimization, a deep understanding of large language model behavior, and the ability to translate business requirements into effective prompt strategies. Key achievements should quantify improvements in model accuracy, reduction in undesired outputs like hallucinations, or efficiency gains in inference costs. Mentioning specific techniques such as Chain-of-Thought prompting, few-shot learning, RAG implementations, and experience with models like GPT-4 or LLaMA, alongside relevant Python scripting or MLOps exposure, is crucial.

This example CV excels at demonstrating these specific requirements. It effectively quantifies prompt engineering achievements, such as "optimized prompts reducing inference costs by 15%" or "improved task-specific model accuracy by 10% through iterative testing." Skills are intelligently categorized, separating "Prompt Design Patterns" from "Technical Skills" to highlight a nuanced understanding. Furthermore, it prominently features relevant certifications, like those from deeplearning.ai, and specific tools used for prompt development and evaluation, ensuring the candidate's practical expertise for deployment and iteration is undeniable.

This template, built with JobSprout, offers a solid foundation for aspiring prompt engineers. It can be easily remixed to create a personalized, impactful resume tailored to individual accomplishments.