Wherever you look, artificial intelligence (AI) is reshaping the business landscape. It supports customer services, delivers powerful analytics, and automates manual processes. However, to use the technology to its fullest, businesses need employees who understand AI.
This is where an Artificial Intelligence Engineer comes into play. But what is an AI engineer? Equally important, what skills should you look for when filling this role? We’ll answer these questions in this guide.
What is an AI engineer?
An AI engineer is a specialist software engineer who plays a vital role in developing AI applications to boost your business. AI tools–from your Global Payments POS solution to your customer service chatbot–can have varied purposes, including assisting with decision-making, increasing productivity, and many other areas.
One key part of AI engineering is understanding machine learning. This is a subset of AI designed to analyze data and learn from it in a similar way to that of humans. AI engineers train machine learning models on large datasets – for instance, a healthcare company might develop an application that analyzes huge amounts of historical medical data to identify risk factors that could lead to a range of health conditions.
AI is complex and to use it effectively, you need experts to develop effective AI tools. To make smart hiring decisions, you need to put a strong focus on AI in recruitment.
Must-have skills to look for in an AI engineer
An AI engineer is a relatively new role for organizations. Need help finding the best candidate? Here are some pointers on skills that your job description should include.
Programming skills
Programming is an important skill for an AI engineer. They’ll need to understand a variety of programming languages to develop AI applications. Make sure that your engineer has experience with the following:
- JavaScript – Utilized by 62.3% of all developers, JavaScript is the most popular programming language in the world. Used primarily in the development of web pages, JavaScript is essential for creating applications that can process large data volumes in real-time.
It can also integrate AI features into your website and create cookies for use after receiving users’ consent to comply with GDPR.
- Python – This is a flexible, platform-independent language that is easy to read. One use, amongst many others, is for implementing machine learning models. Python contains a variety of machine learning frameworks such as Tensorflow, Pandas, and Keras.
- C++ – This has extremely high execution speeds and access to memory management, making it ideal for more complex apps. It is commonly used by VR applications, operating systems, and search engines.
Knowledge of machine learning
All AI engineers must have an understanding of machine learning – after all, it’s at the heart of a lot of AI development.Your candidates should have practical experience with certain frameworks and models. Let’s look at some examples:
Deep learning is one of the latest developments in machine learning technology. Its frameworks attempt to replicate human neural networks. Attention Insight API, for instance, leverages deep learning algorithms that simulate human vision to help website visitors determine how site designs are generally perceived.
AI engineers also develop algorithms to recognize patterns in data, text, or pictures and use this to make predictions.
Of its many potential uses, one is in the area of fraud prevention. Deep learning can recognize previous patterns of behavior that resulted in fraud, informing improved checking regimes.
The more data it analyzes, the better it gets. Engineers must be able to fine-tune their models to maximize effectiveness. Wondering exactly what is AI fine tuning? It’s the process of training AI tools with data relating to specific use cases and industries.
AI analysis isn’t limited to numbers and text. Computer vision software enables the extraction of data from media to interpret picture and video files. Computer vision has many uses – such as using an AI tool to manage product quality by spotting defects or monitoring work practices to identify health and safety risks.
Finally, Transformer models are a powerful subset of deep learning and are some of the newest forms of AI. What is a transformer model? Using this technology, engineers develop AI models to generate text, interpret speech, answer questions, and many other functions.
Communication and Collaboration
Clear and effective communication with both customers and throughout your company will give you a business advantage. So, not only is it important for AI engineers to be able to collaborate effectively with their peers, but they also need to understand how AI can impact communications in general.
In particular, it’s vital they have a strong understanding of a technology called natural language processing (NLP). You can use NLP to understand text-based and audio prompts from humans. So, if you want to boost internal communications or are considering how to set up a call center your engineer can use NLP to help.
For example, an engineer could use NLP to build an intelligent chatbot. This could answer a wide range of customer questions, using historical data to provide intelligent answers. Similarly, they might develop virtual agents, capable of responding to customers on phone calls.
Data Modelling and Analysis
Technical development will be wasted if it doesn’t deliver the desired outcomes. For this, you’ll need strong data modeling and analysis skills.
Analytical skills are vital to enable understanding and formulation of AI applications. They also play a crucial role in the interpretation of outputs. Similarly, data modeling skills have become increasingly important. At its core, this is the ability to understand data objects and their relationship to each other. This supports the ability to create sophisticated data models used in AI programs.
Security
Strong IT security is vital for all modern businesses. Your team will need up-to-date knowledge of tools, threats, legal obligations, and approaches to mitigation approaches. AI can be a powerful aid for combatting cybersecurity threats. The areas it can support include:
- Control assessment/identifying vulnerable areas
- The automation of security tasks
- Protection against phishing and other threats
However, it can also bring its own risks – especially if you’re handling large amounts of data in order to train an algorithm. AI engineers should understand how to protect their data against breaches, as well as make sure they comply with GDPR, HIPAA, and other relevant regulations.
An eagerness to learn
We’ve reflected on some of the technical skills needed for AI engineers. A candidate’s attitude is equally important. Over the next decade, we will see a continued acceleration in new technology and innovation. To stay relevant, businesses need to embrace these changes.
That’s why AI engineers must embrace continuous learning. An engineer should display a willingness to keep up with the latest AI developments.
Get hiring
AI technology continues to develop and becomes ever more powerful. As it delivers richer customer experiences, expectations will increase. Successful businesses will utilize the power of AI. If you don’t want to be left behind, your business will need an AI engineer.
Treat the points in this article as a checklist when assessing applicants and you’ll build a strong, forward-facing team.
So, what are you waiting for? Start looking for the best talent for your business!
Wherever you look, artificial intelligence (AI) is reshaping the business landscape. It supports customer services, delivers powerful analytics, and automates manual processes. However, to use the technology to its fullest, businesses need employees who understand AI.
This is where an Artificial Intelligence Engineer comes into play. But what is an AI engineer? Equally important, what skills should you look for when filling this role? We’ll answer these questions in this guide.
