Today, 77% of users interact with a service or device powered by artificial intelligence. AI applications include chatbots, video surveillance tools, object detection applications, and autonomous systems (e.g., humanoid robots and self-driving cars). AI can help us make more sensible financial decisions and improve our health and wellness. This is just the start: As AI use cases expand and automate repetitive tasks, global AI spending is projected to surpass $500 billion by 2027. In this surge, practically all businesses can benefit from hiring the right AI engineers.

With AI courses and terminology becoming more commonplace among professionals, you may assume that there exists a large talent pool, and hiring an AI engineer is easy. It is not. There’s a big difference between the many software engineers who list AI as a skill on their résumés and capable AI engineers who can add value to a company by building cutting-edge AI systems, integrating them into existing company infrastructure, and guaranteeing they work efficiently.

So, how can you hire artificial intelligence developers who will give your enterprise an AI advantage? Read on to discover the critical skill requirements, tips on crafting an effective job description, and interview questions that will help you identify exceptional candidates.

What Attributes Distinguish Quality AI Developers From Others?

Strong candidates are well versed in the required core technical skills for AI development:

• They have experience developing machine learning (ML) or AI models, and generative AI solutions, from scratch.
• They provide scalable solutions with API development (i.e., turning models into APIs).
• They understand ML algorithms.
• They incorporate DevOps processes into the ML and AI development workflow, and deploy models in a continuous learning pipeline.
• They maintain ML and AI systems infrastructure.
• They can work with big data and perform statistical analysis of data.

Proficiency in at least one programming language (e.g., Python, R, Java, MATLAB) is a must when working with AI. Python is particularly popular for AI development due to its many libraries and ease of use. Strong software development skills will allow engineers to collaborate effectively with teams and implement solutions with the reliability and accuracy required for a production environment.

Additionally, artificial intelligence experts should be familiar with modern large language models (LLMs) and deep learning frameworks (TensorFlow, PyTorch, and Keras), as well as cloud computing platforms (AWS, Microsoft Azure, and Google Cloud Platform). This will ensure that they can deploy AI models at scale and serve enterprise-level projects.

The best AI engineers combine their technical abilities with soft skills—specifically, the ability to evaluate and solve the complex problems that businesses face. Experts who can speak to experience in this area (e.g., designing or contributing to an AI strategy) are top candidates. These engineers recognize the potential of AI technologies to bring unprecedented progress and massive cost savings to organizations.

Look for seasoned AI professionals with several years of 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, though 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.

How Can You Identify the Ideal AI Developer for You?

AI engineers often focus on different specialization areas, and you should choose a developer whose skill set aligns with your project goals.

Individual candidates may have varying levels of expertise across each specialization area. Still, overall, an AI engineer should possess most of these skills and be an expert in at least one area:

Specialization

Required Skills

Applications

Mathematics and statistics

• Comprehension of core mathematical concepts such as linear algebra, calculus, probability, and statistics

• Designing an AI system
• Correcting errors
• Improving an out-of-the-box model’s performance for your particular problem

Machine learning

• Knowledge of ML algorithms (e.g., decision trees, random forests, neural networks, and deep learning)
• Experience with ML libraries (e.g., scikit-learn, TensorFlow, and PyTorch)

• Building efficient models
• Experience with fine-tuning models for specific domains
• Making appropriate decisions when choosing the tools and algorithms to solve the problem at hand

Data structures and algorithms

• Solid understanding of fundamental data structures (e.g., arrays, linked lists, and trees)
• Experience with standard algorithms (e.g., sorting, searching, and optimization algorithms)

• Preparing data
• Extracting relevant features from data
• Feeding the most important features in the model to ensure the model’s performance

Data analysis

• Experience with data science and data analysis techniques (e.g., data cleaning, normalization, and feature extraction) and tools (e.g., pandas and NumPy)

• Extracting insights from data
• Presenting problems and solutions to stakeholders
• Translating real-world problems into mathematical representations

Databases

• Familiarity with SQL databases and NoSQL databases (e.g., MongoDB and Cassandra)
• Experience managing and storing large datasets

• Handling data efficiently
• Ensuring the transparency of AI algorithms
• Presenting project results and intermediate stages effectively
• Integrating with upstream and downstream systems

What is the difference between AI and ML developers?

One common challenge for hiring managers is understanding the difference between AI and ML engineers, especially since AI engineers need various ML skills and may specialize in this area. How do artificial intelligence versus machine learning developers compare? The two professions require similar training, aptitudes, and academic backgrounds (typically in statistics, mathematics, computer science, or engineering). Both of these types of experts may build ML models, analyze data, and build ML pipelines.

However, the role of an AI engineer is broader than that of an ML engineer. It can require the ability to build automated systems based on ML models, and AI engineers may be more involved in computer or data 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, target AI developers for hire who possess the specialization that matches your requirements.

How to Write an AI Developer Job Description for Your Project?

You’ll need to be clear on your organization’s needs to attract skilled developers with your job description. Some companies want engineers who can optimize their organization’s use of artificial intelligence and machine learning, while others look for experts to design and implement AI solutions from scratch. Consider 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 development team the new employee will join. It is also helpful to specify whether you’re looking for on-site, hybrid, or remote AI developers.

With a comprehensive job description complete, the next part of the hiring process is selecting high-quality candidates and interviewing them to assess their fit with your other team members.

What Are the Most Important AI Developer Interview Questions?

To conduct an effective interview, focus on the questions relevant to your business industry and those testing the applicant’s knowledge of the AI ecosystem and processes your AI project will use.

Regardless of project or industry needs, AI experts should be able to enunciate the core value provided by AI and how it works, as these skills are crucial to 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 developers to approach interview answers:

How does AI work?

In general, 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 wider 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. But 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 in specific ways compared to human intelligence, AI solutions are highly efficient in natural language processing (NLP), computer vision (CV), and classical simulation and optimization problems. AI can be used in a wide variety of applications, including sentiment analysis, predictive analytics, automatic speech recognition, and more. It can even help programmers write code. 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.

After assessing a developer’s understanding of broader and project-specific AI topics, you should supplement these questions with additional ones that are specific to your desired AI specialization:

• 10 Essential Data Modeling Interview Questions and Answers
• 10 Essential Machine Learning Interview Questions and Answers
• 19 Essential Algorithm Interview Questions and Answers
• 10 Essential Data Analysis Interview Questions and Answers
• 41 Essential SQL Interview Questions and Answers
• 11 Essential Python Interview Questions and Answers
• 11 Essential Business Intelligence Interview Questions and Answers

There is no one-size-fits-all answer for how to prepare your interview questions, especially when hiring AI experts. Tailoring your topics to your specific project requirements will go a long way in ensuring you hire a quality engineer.

Why Do Companies Hire AI Engineers?

AI will undoubtedly change the future. It has the potential to automate routine manual tasks and help with strategic decision-making, saving companies considerable money. AI may disrupt key industries like consumer marketing, finance, and healthcare: It can personalize the user experience for consumer products, power financial algorithms impacting 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.

AI engineers stay at the forefront of research developments to advise organizations on AI strategy. They may create entire AI processes and strategies, oversee data collection and processing, perform statistical analysis of the data, build and update models, and integrate results into the company’s processes, applications, and systems.

With the increasing scope and accuracy of models and recent developments in NLP and CV, having an AI engineer on your team is pertinent—if not essential—to maintain competitive pricing and innovation, especially for startups looking to disrupt traditional markets.

• If you aren’t sure how AI fits into your business, hiring an experienced AI engineer in a consulting role can help you identify the systems that can be optimized with AI.
• If you already have ML engineers and data scientists on staff, an AI expert can maximize the efficiency of ML model integrations, AI strategy, and processes.

Securing the right AI engineer to address company goals is vital to an organization’s present and future success. With the selection criteria and practical hiring tips covered in this guide, you’ll be prepared to select a new employee for various AI specializations.

The technical content presented in this article was reviewed by Matias Aiskovich.

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