Prompt Engineer: Analyzing Hard and Soft Skill Requirements in the AI Job Market

TL;DR

The paper provides a data-driven snapshot of prompt engineering in the AI job market by analyzing 20,662 LinkedIn postings (including 72 prompt engineer roles). It introduces an LLM-assisted skill extraction pipeline and a deduplication/grouping workflow to reveal a distinctive soft and hard skill profile for prompt engineers, distinct from other data science and ML roles. The study finds prompt engineering to be rare but characterized by a strong emphasis on AI/LLM knowledge, prompt design, agile testing, and robust communication and collaboration, extending beyond traditional text prompting to lifecycle deployment practices. These insights inform job seekers, employers, and educators about evolving requirements and point to curriculum and upskilling needs in the face of rapid LLM adoption.

Abstract

The rise of large language models (LLMs) has created a new job role: the Prompt Engineer. Despite growing interest in this position, we still do not fully understand what skills this new job role requires or how common these jobs are. In this paper, we present a data-driven analysis of global prompt engineering job trends on LinkedIn. We take a snapshot of the evolving AI workforce by analyzing 20,662 job postings on LinkedIn, including 72 prompt engineer positions, to learn more about this emerging role. We find that prompt engineering is still rare (less than 0.5% of sampled job postings) but has a unique skill profile. Prompt engineers need AI knowledge (22.8%), prompt design skills (18.7%), good communication (21.9%), and creative problem-solving (15.8%) skills. These requirements significantly differ from those of established roles, such as data scientists and machine learning engineers. Our findings help job seekers, employers, and educational institutions in better understanding the emerging field of prompt engineering.

Figures (5)

• Figure 1: Prompt for skill extraction from job postings. Source: Authors own work.
• Figure 2: Skills cluster distribution for all analyzed jobs.
• Figure 3: Comparative distribution of skills clusters across job roles. Source: Authors own work.
• Figure 4: Skills cluster distribution for prompt engineer positions, in comparison to skills in other jobs. Source: Authors own work.
• Figure 5: Skills co-occurrence between soft and hard skills for prompt engineer positions. High values indicate pairs of skills that are often mentioned together in job descriptions, whereas low values indicate the opposite. Values of $|r| > 2.0$ indicate significant deviations from expected values ($p < 0.05$). Source: Authors own work.