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An Empirical Analysis of Job Ads in The  \nNetherlands  \nMarcel Meesters  \n[m.meesters@fontys.nl](m.meesters@fontys.nl)[ ](m.meesters@fontys.nl)Fontys University of Applied Science Eindhoven, Netherlands  \nPetra Heck  \n[p.heck@fontys.nl](p.heck@fontys.nl)  \nFontys University of Applied Science Eindhoven, Netherlands  \nAlexander Serebrenik  \n[a.serebrenik@tue.nl](a.serebrenik@tue.nl)[ ](a.serebrenik@tue.nl)Eindhoven University of Technology Eindhoven, Netherlands  \nABSTRACT  \nRecently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role ofAI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually.  \nThe job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development, 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills.  \nOur research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.  \nKEYWORDS  \ndata science, software engineering, AI engineer, ML engineer, job market, job ad  \nACM Reference Format:  \nMarcel Meesters, Petra Heck, and Alexander Serebrenik. 2022. What Is an AI Engineer? An Empirical Analysis of Job Ads in The Netherlands. In 1st Conference on AI Engineering-Software Engineering for AI (CAIN’22),  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions [from permissions@acm.org](from permissions@acm.org).  \nCAIN’22, May 16–24, 2022, Pittsburgh, PA, USA © 2022 Association for Computing Machinery.  \nACM ISBN 978-1-4503-9275-4/22/05. . . $15.00 [https://doi.org/10.1145/3522664.3528594](https://doi.org/10.1145/3522664.3528594)  \nMay 16–24, 2021, Pittsburgh, PA, USA. ACM, New York, NY, USA, 9 pages. [https://doi.org/10.1145/3522664.3528594](https://doi.org/10.1145/3522664.3528594)  \n1 INTRODUCTION  \nAI has gained increasing interest and adoption in the IT industry [2] . Professional development and deployment of software systems that incorporate AI features is challenging and requires skilled engineers [1] . This software engineering for AI-enabled systems is also called “AI engineering” [2]. A 201","cbCaify6wbXAGpzY","https://ap.wps.com/l/cbCaify6wbXAGpzY","pdf",792065,9,"English","# Abstract\n# Introduction\n## Research questions\n## Key findings","[{\"question\":\"How was the job ad data collected and analyzed in the study?\",\"answer\":\"Job ads were retrieved from Jobfeed (TextKernel) for the period April 2018 to April 2021. The authors selected 367 ads using a process similar to primary-study selection in a literature review and then manually coded 125 ads to support the research questions.\"},{\"question\":\"What job task categories are most common for AI engineers?\",\"answer\":\"The study finds AI engineer tasks cluster into five categories: business understanding, data engineering, modeling, software development, and operations engineering.\"},{\"question\":\"What three AI-engineer profiles does the study report?\",\"answer\":\"The paper reports three distinct profiles: a data science engineer focused on modeling, an AI software engineer focused on software development, and a generalist AI engineer covering both models and software.\"}]","What Is an AI Engineer? An Empirical Analysis of Job Ads in The Netherlands - Paper | PDF",1789319642]