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.

I   What tasks does a deep learning engineer carry out?

Deep learning engineers carry out data engineering, modeling, and deployment tasks as shown in Figure 1. This includes:

• data engineering subtasks such as defining data requirements, collecting, labeling, inspecting, cleaning, augmenting, and moving data.
• modeling subtasks such as training deep learning models, defining evaluation metrics, searching hyperparameters, and reading research papers.
• deployment subtasks such as converting prototyped code into production code, setting up a cloud environment to deploy the model, or improving response times and saving bandwidth.

II   What skills does a deep learning engineer need?

Deep learning engineers demonstrate solid scientific and engineering skills (see Figure 2). Communication skills requirements vary among teams. They write both prototyping and production code, as opposed to data scientists who mostly write prototyping code and software engineers who write lots of production code.

Compared to machine learning engineers, this role requires deep learning knowledge in addition to the skills profile presented in Figure 1. It focuses on applications, usually powered by deep learning, such as speech recognition, natural language processing, and computer vision. Hence, it requires skills specific to deep learning projects such as understanding and using various neural network architectures such as fully connected networks, CNNs, and RNNs.

If you’re interested in comparing your skills to other deep learning engineers, we recommend taking the standardized machine learning, deep learning, data science, mathematics, algorithmic coding, and software engineering tests on Workera. If you’re a company hiring deep learning 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 deep learning engineer use?

Deep learning 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.

• Modeling is primarily done in Python using packages such as numpy, scikit-learn, pandas, matplotlib, TensorFlow, and PyTorch.
• Data engineering happens in Python and/or SQL or other domain-specific query languages.
• Deployment is implemented using an object-oriented programming language (such as Python, and Java, C++,) and cloud technologies such as AWS, GCP, and Azure.
• Collaboration and workflow is managed with a version control system (for instance, Git, Subversion, and Mercurial), a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook, and Sublime, and an issue tracking product like JIRA.

IV   In what team structure does a deep learning 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.

Deep learning engineers are versatile individuals. Their mix of engineering and scientific skills allow them to carry out most tasks of an AI project lifecycle. They combine well with data analysts who focus on translating statistics into actionable business insights and software engineers who build the tools and infrastructure that increases the effectiveness of all tasks.

Conclusion

This article aims to clarify what a deep learning 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 role as deep learning engineer, software engineer - machine learning, software engineer - deep learning, software engineer, data scientist, algorithm engineer, research scientist, research engineer, full-stack data scientist, 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.