CAIN 2022
Mon 16 - Tue 17 May 2022
co-located with ICSE 2022
Tue 17 May 2022 06:45 - 07:00 at CAIN main room - AI Engineering Practices Chair(s): Ipek Ozkaya

Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI 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 upon 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. The 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. Our 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.

Tue 17 May

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06:00 - 07:30
AI Engineering PracticesCAIN 2022 at CAIN main room
Chair(s): Ipek Ozkaya Carnegie Mellon Software Engineering Institute
06:00
15m
Research paper
Towards a Roadmap for Software Engineering for Responsible AIResearch Paper
CAIN 2022
Qinghua Lu CSIRO’s Data61, Liming Zhu CSIRO’s Data61; UNSW, Xiwei (Sherry) Xu CSIRO Data61, Jon Whittle CSIRO's Data61 and Monash University, Zhenchang Xing Australian National University
06:15
15m
Research paper
AI Governance in the System Development Life Cycle: Insights on Responsible Machine Learning EngineeringResearch Paper
CAIN 2022
Samuli Laato University of Turku, Teemu Birkstedt University of Turku, Matti Mäntymäki University of Turku, Matti Minkkinen University of Turku, Tommi Mikkonen University of Helsinki
06:30
15m
Research paper
The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI ProjectsResearch Paper
CAIN 2022
Rimma Dzhusupova McDermott, Jan Bosch Chalmers University of Technology, Helena Holmström Olsson Malmö University
06:45
15m
Research paper
What Is an AI Engineer? An Empirical Analysis of Job Ads in the NetherlandsResearch Paper
CAIN 2022
Marcel Meesters Fontys University of Applied Sciences, Petra Heck Fontys University of Applied Sciences, Alexander Serebrenik Eindhoven University of Technology
Pre-print
07:00
15m
Research paper
Data is about Detail: An Empirical Investigation for Software Systems with NLP at CoreResearch Paper
CAIN 2022
Anmol Singhal TCS Research, Preethu Rose Anish TCS Research, Pratik Sonar TCS Research, Smita Ghaisas TCS Research
File Attached
07:15
15m
Other
Discussion on AI Engineering Practices
CAIN 2022


Information for Participants
Tue 17 May 2022 06:00 - 07:30 at CAIN main room - AI Engineering Practices Chair(s): Ipek Ozkaya
Info for room CAIN main room:

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