Developing an AI project development life cycle involves five distinct tasks. No single individual has enough skills (or time) to carry out all tasks in AI project development. Thus, teams include individuals who focus on part of the cycle. Here is a visual representation of six technical roles and how they relate to various tasks.

Figure 1: Technical roles in an AI team vs. the tasks they carry out in AI projects.

I   What tasks does a software engineer carry out?

Software engineers carry out data engineering, modeling, and business analysis tasks as shown in Figure 1. This includes:

II   What skills does a software engineer need?

Software engineers demonstrate outstanding coding and software engineering skills (see Figure 2). Communication skills requirements vary among teams. They mostly write production code, as opposed to data scientists who mostly write prototyping code.

Figure 2: A visual representation of the software engineer’s skill set and level of proficiency.

If you’re interested in comparing your skills to other software engineers, we recommend taking the standardized algorithmic coding, and software engineering tests on Workera. If you’re a company hiring software engineers, you can administer computerized tests to AI job applicants for free using Workera Test and connect with AI practitioners using Workera Connect.

III   What tools does a software engineer use?

Software engineers in different companies use different tools, but some tools stand out. The following tools grouped by task are the most frequently used tools identified in our research.

IV   In what team structure does a software engineer fit?

Building an AI team requires bringing together complementary individuals who can progressively carry out the tasks of the AI project development lifecycle. AI teams focus on data engineering and modeling from the beginning, because they need to validate the feasibility of an AI project or idea. As the project becomes more mature, the team starts focusing on deployment, business analysis, and AI infrastructure.

Software engineers work well with people in charge of modeling, deployment, business analyses. Software engineers build the tools and infrastructure that increases the effectiveness of all tasks while scientists prototype solutions to prove a concept.


This article aims to clarify what a software engineer is, what tasks they carry out, and what skills they need. If you’re an AI practitioner, we hope it helps you choose a career track.

Companies may refer to this position as data engineer, software engineer, software development engineer, software engineer-AI Infrastructure, software engineer-data, and many more titles. If you’re a hiring manager, we hope that it helps you define your job requirements.

AI organizations are constantly evolving, so this article is a work in progress. We intend to revise it as our team learns more about new roles.

Developing an AI project development life cycle involves five distinct$:$ data engineering, modeling, deployment, business analysis, and AI infrastructure.


  1. Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at


  1. The layout for this article was originally designed and implemented by Jingru Guo, Daniel Kunin, and Kian Katanforoosh for the AI Notes, and inspired by Distill.


  1. You can practice for the machine learning test, the deep learning test, the data science test, the mathematics test, the algorithmic coding test, and the software engineering test in The Skills Boost.
For members
Want evaluate and credential your skills, or land a job in AI?
For companies
Are you hiring AI engineers and scientists?

↑ Back to top