Which AI skills are most in demand?

September 24, 2021

AI Skills in demand

Source: iStock/Andy

Artificial intelligence (AI) is transforming societies and economies. It enhances the speed, efficiency and precision of human efforts and is being adopted across most industries.

However, according to a survey by the European Commission, businesses feel that access to the right skill sets is the biggest impediment to adopting AI.

As the artificial intelligence workforce continues to expand, it’s important for tech professionals to consider honing their skills in certain areas if they want to pursue a career in AI. Knowing which skills are the most in demand can help candidates work out what they need to develop for roles such as machine learning engineer or data scientist.

Read on to find out more about the key AI skills in demand.

Types of AI

Here are the various types of AI to develop skills in:

Machine learning

Machine learning is a subset of AI that allows systems to automatically learn and improve from experience without explicit programming. Machine learning uses different algorithms, such as neural networks (which we’ll discuss later) to solve problems.

Deep learning

Deep learning is a specific type of machine learning that imitates the way humans obtain particular knowledge. It is an important component of data science and includes statistics and predictive analytics. Examples of deep learning include face recognition, virtual assistants and vision enablement for driverless cars.

Neural network architectures 

Neural networks make up part of the deep learning process and are inspired by the structure of the human brain. They are complex structures created from artificial neurons that can process multiple inputs and produce a singular output. Understanding this architecture is essential for deep learning.

Computer vision

Computer vision involves training computers to understand and interpret the visual world. This includes accurately identifying and locating objects, then reacting to what they see through the use of digital images from deep learning models, videos and cameras.

Programming languages and intelligence skills

beautiful-male-computer-engineer-and-scientists-create-neural-network-picture-id1182697690 Source: iStock/gorodenkoff

Learning specific programming languages is essential for work in the AI field. Python, R, Java and C++ are the most commonly sought after.


Python is one of the most popular programming languages in use at the moment. It was created in 1991, and has since been one of the most widely used programming languages, alongside Java and C++.

It is useful for AI because it is reliable, simple to use and increases execution time. Python requires the least code compared to other languages and has pre-built libraries for scientific computation, advanced computing and machine learning.


R is commonly used in new-style AI that involves statistical computations, machine learning, numerical analysis, neural networks and the use of Bayesian inference. 

This language allows you to gather and clean up datasets, use linear algebra and matrix transformations to process data, and apply statistical and machine learning functions. 

HackerRank has an online certification course to teach you how to use R.


Java is another of the most commonly used programming languages for AI projects. It is used mostly for AI intelligence programming, machine learning solutions, multi-robot systems, genetic programming, neural networks and search algorithms.

You can learn Java programming on the HackerRank site.


One of the oldest and fastest programming languages, C++ can manipulate hardware resources and provide procedural programming for CPI intensive applications. It can be used to develop browsers, operating systems and games. C++ supports various programming methods such as procedural, functional and object-oriented functions, making it a very flexible programming language.

Libraries and frameworks

Besides programming languages, you need to be able to understand and apply the following concepts and techniques in your work, regardless of programming language.


TensorFlow is an open source platform for machine learning that provides a wealth of resources, tools and libraries to enable developers to build machine learning applications.

It was built by Google with the C++ programming language, but coders can also use Python when developing applications for this AI engine.


NumPy is a fundamental package for scientific computing in Python. It facilitates advanced mathematical operations on large data sets, which are otherwise typically executed with less code than is possible within Python’s existing sequences.

NumPy provides a multidimensional array object and variations like matrices and masks. These can be used for a number of mathematical operations.


An open-source library within Python, SciPy is used to solve mathematical and scientific problems. It is built upon the NumPy extension and enables users to visualise and manipulate data with a variety of high-level commands.


Scikit-learn is considered one of the most robust and useful libraries for machine learning in Python. It includes a plethora of tools for statistical modelling and machine learning, such as dimension reduction, classification, clustering and regression, all through a consistent interface in Python.


PyTorch is most commonly used for deep learning applications using CPUs and GPUs. It is an open source machine learning library for Python, developed predominantly by the Facebook AI research team.

Apache Spark

Apache Spark is a distributed, open source processing system that is used for big data workloads. It uses optimised query execution and in-memory caching for analytic queries against all sizes of data.

Continual AI skills development

It is also important to highlight the need to constantly improve existing knowledge and learn new technologies when working in AI. A survey by Stack Overflow asked developers how frequently they learn a new language or skill, and 75% said they do this once every few months or once a year.

Screenshot 2021-09-24 at 15.22.21

This shows us how even the best candidates need to adapt and learn constantly to keep up.

Areas that are useful to develop include brand new programming languages such as Triton and Julia, as well as more established languages like GEM, which was established in 1985.

Domain knowledge 

Having specialist technical knowledge and expertise in machine learning and artificial neural networks is a good start to becoming an ML engineer, but many companies expect more. 

For example, engineers should have domain experience in their business. This knowledge helps them to understand the specifics of the industry and any challenges the business is trying to resolve.

Soft skills


Source: iStock/gradyreese 

In addition to the technical skills necessary to work in AI engineering, it is also essential to have soft skills that complement that technical knowledge. These include:

Communication - this means being able to accurately and clearly communicate ideas across the business, using the correct tone and body language to deliver your messaging. 

Ability to work as a team - engineers will also need to be able to manage teams where necessary, as well as work closely with other teams (such as robotics, electrical engineering and software engineering) to run and implement AI projects. 

Creativity - AI requires new ways of thinking, which means creativity is an essential skill for engineers in this field. 

Analytical thinking - Critical thinking skills allow engineers to come up with innovative ideas and solve complex problems using logic and reasoning.

Find your next AI job with Airswift

Here at Airswift, we have more than 40 years of experience in helping candidates find jobs with some of the world’s biggest companies. 

With our unrivalled global presence, understanding of tax regulations, labour and payroll, we can support you at every stage of the recruitment process. 

Get started today! Register with our job board and set up alerts for notifications tailored to your job search.

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This post was written by: Andrew McLoughlin, Senior Manager at Airswift