Today, an astounding 77% of consumer services and devices are driven by artificial intelligence. AI applications span from chatbots and video surveillance tools to object detection systems and autonomous technologies like humanoid robots and self-driving cars. AI can aid in making smarter financial decisions and enhancing our health and wellness. This is just the beginning: as AI use cases grow and automate repetitive tasks, global AI spending is projected to exceed $300 billion by 2026. Amid this surge, virtually every business stands to gain by hiring the right AI engineers.
An artificial intelligence icon over a global map, with blue developer icons in various locations and a magnifying glass over a green developer icon.
While AI courses and terminology are becoming more familiar to professionals, hiring an AI engineer is far from straightforward. There’s a significant difference between software engineers who list AI as a skill on their résumés and capable AI engineers who can build sophisticated AI systems, integrate them into existing infrastructures, and ensure their efficient operation.
So, how do you hire AI developers who can give your enterprise an AI edge? Read on to discover the critical skills required, job description tips, and interview questions that will help you find exceptional candidates.
What Attributes Distinguish Quality AI Developers from Others?
Strong candidates are proficient in the core technical skills necessary for AI development:
- They have experience developing machine learning (ML) or AI models from scratch.
- Provide scalable solutions with API development (i.e., turning models into APIs).
- Understand ML algorithms.
- Automate ML and AI processes and deploy models in a continuous learning pipeline.
- Maintain ML and AI systems infrastructure.
- Work with big data and perform statistical analysis of data.
Proficiency in at least one programming language (e.g., Python, R, MATLAB) is essential when working with AI. Python is particularly popular for AI development due to its extensive libraries and ease of use. Strong programming skills enable engineers to collaborate effectively with teams and implement solutions with the reliability and accuracy needed for a production environment.
Additionally, artificial intelligence experts should be familiar with modern deep learning frameworks (e.g., TensorFlow, PyTorch, and Keras) and cloud computing platforms (e.g., Amazon Web Services, Microsoft Azure, and Google Cloud Platform). This ensures they can deploy AI models at scale and handle enterprise-level projects.
The best AI engineers combine their technical abilities with soft skills—specifically, the ability to evaluate and solve critical business problems. Experts who can demonstrate experience in this area (e.g., designing or contributing to an AI strategy) are top candidates. These engineers understand AI tools’ potential to bring unprecedented progress and significant cost savings to organizations.
Look for seasoned AI professionals with experience in your industry or across multiple sectors. Review the projects they have worked on and completed to confirm their fit. Many candidates list their portfolio projects on their résumés, but you may also request that they provide GitHub links with their application or in a cover letter. Publicly available portfolio projects highlight the experience and accomplishments of top AI engineers.
What is the Difference Between AI and ML Developers?
A common challenge for hiring managers is distinguishing between AI and ML engineers, especially since AI engineers require various ML skills and may specialize in this area. How do artificial intelligence and machine learning developers compare? The two professions require similar training, aptitudes, and academic backgrounds (typically in statistics, mathematics, computer science, or engineering). Both types of experts may build ML models, analyze data, and develop ML pipelines.
However, the role of an AI engineer is broader than that of an ML engineer. It can involve building automated systems based on ML models, and AI engineers may be more engaged in computer engineering tasks. AI engineers may also be responsible for setting the direction of a corporation’s AI strategy and managing the AI infrastructure. Unless you are looking to hire machine learning engineers, you should target AI developers who possess the specialization that matches your requirements.
How to Write an AI Developer Job Description for Your Project
To attract skilled developers with your job description, you need to be clear about your organization’s needs. Some companies want engineers who can optimize their use of artificial intelligence and machine learning, while others seek experts to design and implement AI solutions from scratch. Determine whether you need an AI consultant to define a new strategy or a full-time staff member who will be deeply involved in all aspects of your corporate operations.
Next, write your job description with critical project details in mind. Describe the project’s budget, timeline, relevant business context, and existing software technologies. Identify the desired outcomes you want from the AI engineer’s work and provide an overview of the team the new employee will join.
With a comprehensive job description complete, review and select the best candidate profiles and conduct interviews to assess their fit with the team.
What are the Most Important AI Developer Interview Questions?
To conduct an effective interview, focus on questions relevant to your business industry and those that test the applicant’s knowledge of the AI ecosystem and processes your project will use.
Regardless of project or industry needs, AI experts should be able to articulate the core value provided by AI and how it works, as these skills are crucial for convincing stakeholders of the importance of AI solutions. The following examples provide a model for these types of questions and how you might expect experienced AI developers to approach interview answers:
How Does AI Work?
AI can be defined as software that mimics human thinking and decision-making. It works by using actual or artificially created data to match questions to potential answers based on ML and statistical algorithms. These questions and answers include both those posed in natural language and a broader set of questions like determining the next location of a vehicle or recognizing objects in a video clip. In the past, AI was used to create expert systems by assembling all possible answers to all possible questions, a method with storage and time constraints. However new ML advances have allowed for a strategy that provides questions and answers that are not an exact match but a probabilistic pair, allowing AI to answer a broader range of questions—albeit with varying accuracy.
What are the Main Applications of AI?
While the range of AI capabilities is limited compared to human intelligence, AI solutions are highly efficient in natural language processing (NLP), computer vision (CV), and classical simulation and optimization problems. Experienced developers should understand the best ways to help your organization optimize processes and build AI solutions based on your industry and products.
Why is AI Important?
AI can potentially add up to a 14% GDP boost globally by 2030. It is already widely used by many businesses in healthcare, crime systems, knowledge synthesis, transportation, security, and finance. Artificial intelligence experts who will spearhead new AI initiatives should be able to speak to existing AI uses and benefits to convince stakeholders of its importance.
Since AI solutions vary across industries and project needs, it is vital to ask a developer how they plan to address your specific problems.
What Type of Model Would You Recommend for Our Business Needs?
An engineer should choose an algorithm and appropriate tools based on a company’s specific problem and data. For instance, certain models (e.g., decision trees) are suitable for handling tabular data resembling spreadsheets, while others (e.g., deep neural networks) excel at processing unstructured data like images or audio.
How Would You Measure the Performance of Your AI Solution?
Candidates must always keep in mind the objective of delivering tangible business value and establish a means of quantifying that value. Look for developers who provide a specific plan for measuring the performance of machine learning models with business-relevant metrics. For instance, when predicting customers at risk of churn, it is essential to involve subject matter experts from the business domain. Their insights can help identify client segments that generate higher revenue, ensuring that the model excels at detecting such valuable customers. Merely detecting at-risk clients with low value would not prove useful in this scenario.
How Will You Ensure Your Model’s Quality Over Time?
The performance of artificial intelligence and machine learning models can vary over time as the underlying context and circumstances for which the models were trained evolve. For example, a revenue prediction model may have experienced a significant increase in errors during the onset of the COVID-19 pandemic. Candidates should outline a clear monitoring plan to track the performance of ML models over time and promptly identify any degradation in their performance.
Why Do Companies Hire AI Developers?
AI is poised to revolutionize the future. It has the potential to automate routine tasks and aid in strategic decision-making, saving companies considerable money. AI may disrupt key industries like consumer marketing, finance, and healthcare: it can personalize consumer products, power financial algorithms that impact markets and risk management, and improve healthcare diagnoses (e.g., AI-powered personal wellness assistants that monitor our health or computer vision systems that assist with surgery and disease diagnosis).
The increasing use of AI—and the resulting demand for talent—is clear, but what does an artificial intelligence engineer do, exactly? AI developers are the force behind a growing variety of real-world AI applications: task automation, chatbots, fraud detection, risk assessments, medical diagnoses, personalized marketing and customer services, image recognition, autonomous vehicles, customized learning experiences, video games, and more.
