Considering a career in AI? Start here
If you think Artificial Intelligence (AI) is here to take over your job, think again.
Even though there are many questions around AI control and alignment (with the goals of our society), so far this technology is making some jobs easier and even creating new ones.
Thanks to the rising commoditisation of AI technology, AI jobs are in high demand. In fact, AI is projected to create 97 million jobs in the coming years. By 2030, AI could contribute up to $15.7 trillion to the global economy.
There’s never been a better time to consider an AI career than right now. Here’s how to get started.
AI jobs in 2024 (and beyond)
No matter your industry, there’s no hiding from AI’s influence. Each class of artificial intelligence has augmented every major industry on the globe and helps boost productivity like nothing before it. From designers and doctors to manufacturers and engineers (and even AI-powered influencers and AI music popstars), AI is running the backbone of our digital society – which is good news for those looking to work with the technology.
Here are a few AI jobs you can explore:
AI Research Scientist
AI Research Scientists focus on advancing the field of artificial intelligence through in-depth research and experimentation. They design and develop innovative algorithms, models, and techniques to enhance AI systems’ capabilities.
An AI Research Scientist may work on improving natural language processing (NLP) algorithms to enhance virtual assistants like Siri or Alexa, making them more context-aware and responsive. A deep understanding of AI and intelligent automation is essential, so if you’re looking for a good place to start try reading our best AI study books.
Data Scientist
The role of a Data Scientist blends aspects from technical jobs like a mathematician, scientist, statistician, and computer programmer to analyse large sets of structured and unstructured data to extract valuable insights and support decision-making. They utilise statistical models, advanced mathematics, machine learning, and data visualisation tools to interpret complex datasets commonly used to inform critical business decision-making.
For example, a Data Scientist in an e-commerce company will analyse customer behaviour data to identify patterns, helping to optimise product recommendations and personalise the online shopping experience.
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning algorithms and models. They work on deploying these models into applications, systems, or products to enable them to make intelligent, data-driven decisions.
A good example of a Machine Learning Engineer’s role at a ride-sharing platform might like Uber may look like this: they will develop algorithms that predict demand in different locations and optimise driver dispatch systems for efficient and timely pickups.
Deep Learning Engineer
Deep Learning Engineers specialise in creating and implementing deep neural networks. They work on complex architectures for deep learning models, often applied to image recognition, natural language processing, and other intricate tasks.
On an average workday, a Deep Learning Engineer might contribute to developing facial recognition technology used in smartphone security systems or surveillance systems, for example.
Robotics Scientist
Robotics Scientists research and develop technologies for designing and controlling robots. They focus on creating systems that can perceive, interact with, and adapt to their environments, often incorporating AI and machine learning.
A Robotics Scientist may develop autonomous drones used for surveillance or delivery services and work to enable them to navigate and respond to changing conditions.
Prompt Engineer
Prompt Engineers specialise in crafting the right questions or instructions to guide AI models, especially Large Language Models (LLMs) and GenAI, to produce desired outcomes. They focus on natural language processing and design (and refine) the prompts that users interact with to yield desired outputs from AI systems.
In the context of chatbots, a Prompt Engineer might work on crafting prompts that elicit accurate responses, ensuring a more conversational and user-friendly interaction in customer support scenarios.
Skills needed for a career in AI
With amazing salaries and strong growth potential, a career in AI is certainly appealing to anyone looking to play an active role in developing our digital society in the future. As a result, there are certain skills and know-how required to start your career in AI.
Technical knowledge
Building a career in AI demands a strong foundation in technical skills. You need to be proficient in programming languages like Python and R to be able to implement AI algorithms. A solid understanding of statistics and mathematics is also crucial for developing accurate AI models.
Additionally, you will also need to be familiar with machine learning (ML) algorithms and frameworks, such as TensorFlow and PyTorch to help you design and deploy sophisticated AI solutions.
Experience working with data
A cornerstone skill for aspiring AI professionals is hands-on experience with data. You need to be able to gather, preprocess, and analyse large datasets. This helps you bring out meaningful insights, identify patterns, and train machine learning models effectively.
It is also vital to have some practical expertise in data manipulation tools and techniques to ensure a capable foundation for all of your AI-related tasks.
Staying up-to-date
AI is constantly evolving, so staying informed about the latest trends and advancements is critical to ensure you’re utilising the technology to its full capabilities. It is important to actively engage with the AI community, follow research publications, and view conferences or talks from thought leaders to remain at the forefront of industry developments.
Continuous learning and adaptation to emerging technologies will help you apply the most innovative solutions to real-world challenges.
Degree or certification
Most AI jobs will require you to have a solid educational background in the form of a formal degree in computer science, engineering, or a related field. Certifications also play a crucial role in establishing credibility in the AI domain.
However, not all certifications are the same. It is important to acquire certifications from recognised institutions or platforms. Both degrees and certifications contribute to a well-rounded profile for a successful career in AI.
Get job hunting
This should have you covered to get started in your AI job hunt to explore a career in an industry that is shaping our world.
While many of these jobs didn’t exist a few years ago, the evolution of the industry will no doubt keep sprouting new and exciting career opportunities in the future. There’s so much to be excited about when it comes to working with AI. Good luck!
Mario Grunitz
Working Machines
An executive’s guide to AI and Intelligent Automation. Working Machines takes a look at how the renewed vigour for the development of Artificial Intelligence and Intelligent Automation technology has begun to change how businesses operate.
