Artificial intelligence (AI) has been widely used in many fields, finding its applications in virtual assistants, autonomous vehicles, automatic healthcare diagnostics, personalized recommendations, and more.
Hostinger estimated that, in 2023, more than 35% of companies integrated AI to enhance their business operations. This trend is projected to continue into 2024, with an even more significant increase in AI adoption.
However, companies face a challenge: AI technology is complex, yet there’s a shortage of qualified professionals for cutting-edge research and engineering. With this shortage predicted to persist for the next decade, many hiring teams find it difficult to hire AI developers.
So, if you have ideas about an AI project and are seeking AI specialists to assist your state-of-the-art software development or boost operational efficiency but don’t know how to go about hiring them, you’re landing at the right place. In this article, we’ll walk you through all the essential details on how to hire AI engineers effectively.
Let’s get cracking!
Who Are AI Developers?
Simply put, AI developers specialize in creating software or applications that use AI and machine learning (ML) to perform tasks typically requiring human intelligence. They’re familiar with a range of technologies, tools, and programming languages.
There are different job titles for AI developers, like Machine Learning Engineer, Data Scientist, and AI Research Scientist. Each title focuses on different skills.
Now, below are the common tasks of AI engineers:
- Algorithm development:
AI developers are responsible for creating algorithms that enable machines to perform intelligent tasks. This involves understanding different machine learning methods like supervised and unsupervised learning, reinforcement learning, and deep learning.
- Model training and evaluation:
Training machine learning models is an important component of AI engineers’ job. They utilize relevant data to train models and tweak parameters to enhance performance. They also thoroughly test the models to ensure their accuracy and efficiency. Furthermore, they are accountable for ongoing refining to quickly adapt to changing data and business requirements.
- Data preprocessing and feature engineering:
AI engineers also take care of getting data ready for machine learning. They carefully clean and adjust data, handle missing values, and engineer features that help the model find useful patterns. This process, known as data preprocessing, is crucial for making sure the data going into the machine learning system is of high quality.
- AI model deployment:
Artificial intelligence developers don’t just create models; they also deploy them into production environments. This means smoothly adding models into software systems, ensuring they can handle different levels of work, dealing with speed, using resources efficiently, constantly monitoring how well the model works over time in real-world situations, and more.
- Collaboration with cross-functional teams:
Artificial intelligence engineers are also expected to work with diverse teams, including data scientists, domain experts, and software engineers. This collaboration helps them understand what a business needs, figure out tricky details in specific fields, and align AI solutions with the organization’s goals.
What Are the Key Skills of Good AI Developers?
Of course, we all want to hire the best AI developers for your team. But what does it take to be considered a good AI developer?
Below, we’ve listed 7 important AI developer skills that will help you choose the most suitable candidates.
Technical Skills
1. Programming Skills
Building AI systems combines software development and AI technology implementation. That’s why AI engineers need to have a solid understanding of computer science fundamentals and strong programming skills, especially in Python, C++, R, and Java.
They should also be well acquainted with the latest algorithms and frameworks necessary for building AI systems from scratch.
2. Machine Learning Techniques and Algorithms
A strong grasp of machine learning algorithms proves vital for AI developers.
They should be well-versed in popular ML frameworks such as TensorFlow, Keras, and PyTorch. These tools can be tricky to use and require a solid understanding of concepts like linear algebra, probability theory, optimization, and statistics.
3. Data Science Skills
With the increasing reliance on big data, AI developers need strong data engineering skills.
They should be good at analyzing large datasets using tools like Spark and Hadoop or libraries such as R, Python, Java, or Scala. Plus, predictive analytics and insights extraction from past data via public APIs are also crucial abilities.
4. Proficiency in Conceptual Thinking
Since AI is still in its early stages towards true machine intelligence, developers must be able to think conceptually and creatively to come up with innovative solutions.
They also need to be able to tackle problems from different angles and anticipate unknown factors that may arise during development.
5. Deep Understanding of AI and Human Behavior
AI developers should possess a profound understanding of human psychology to incorporate human behavior into building artificial intelligence systems.
They are also expected to be adept at recognizing patterns and trends within human behavior before adjusting AI systems accordingly.
Soft Skills
6. Problem-Solving Skills
Soft skills, including problem-solving and project management abilities, are of great significance in determining a good AI developer. Therefore, AI engineers should be able to manage different technical hurdles and team-related issues during the creation and implementation of new AI models, even unexpected urgent ones during testing.
Additionally, they should be capable of breaking down complex problems into manageable parts and bringing feasible results.
7. Flexibility and Quick Learning
AI projects often demand collaboration with specialists from diverse fields. Take MLOps projects as an example. They require AI engineers, software developers, and the IT department to work together to ensure a smooth pipeline. Thus, being flexible and adaptable to others’ viewpoints and expertise is vital for seamless teamwork.
Furthermore, the capacity to swiftly adapt problem-solving strategies and acquire new skills or knowledge to overcome obstacles is an invaluable trait of an excellent AI developer.
3 Common Ways to Hire AI Engineers
When assembling a dedicated AI development team, organizations typically employ three hiring approaches: in-house recruiting, hiring freelancers, and using recruiting agencies.
Each method comes with strengths and weaknesses so that you can select the best fit for your organization.
1. In-house Recruiter
In-house recruiting stands as a holistic solution for companies seeking a dedicated talent acquisition strategy. These recruiters, being generalists, can seamlessly integrate AI talents into the company’s structure by grasping the nuanced requirements of AI roles well.
However, establishing an in-house team entails higher upfront costs and efforts, which might pose challenges for smaller organizations.
Therefore, it’s essential to explore platforms like GitHub, Stack Overflow, and AI conferences to identify and engage potential candidates who are aligned with the company’s objectives.
Pros:
- Comprehensive Recruitment Management: In-house recruiters are skilled at managing the entire recruitment process. They comprehend the company’s culture, values, and specific role requirements, fostering better alignment between candidates and the organization.
- Cost-Effectiveness for Steady Talent Demand: If there’s a consistent demand for AI talent within the organization, an in-house recruiter can be more cost-effective in the long run compared to outsourcing. In addition, they are capable of cultivating relationships with potential candidates over time, establishing a valuable talent pipeline.
Cons:
- Higher Upfront Costs and Efforts: Establishing an in-house recruitment team incurs higher upfront costs and efforts, including recruitment tools, training, and salary expenses. For smaller organizations or those with sporadic hiring needs, this might not be the most efficient option.
- Inconsistent Hiring Needs: During economic uncertainty or inconsistent hiring needs, maintaining a full-time, in-house recruiter might not be justifiable. This lack of demand could result in the underutilization of resources.
Tips:
- Host Competitions and Explore Platforms: To attract top AI talent, consider organizing competitions on platforms like Kaggle or TopCoder. Additionally, explore community-driven platforms like GitHub, Stack Overflow, and Medium to discover potential candidates actively working on AI-related projects.
2. Hire a Freelance or Contract AI Developer
Opting to recruit freelance AI developers provides organizations with speed and flexibility, which can be particularly advantageous during uncertain economic conditions. Freelancers offer a swift solution for specific projects or tasks, enabling businesses to access specialized skills without committing to long-term engagements.
Pros:
- Speed and Flexibility: Hiring freelance AI developers is fast and flexible, making it ideal for short-term projects or tasks. This agility is beneficial when there’s a sudden need for specialized skills.
- Cost-Effective for Specific Projects: Freelancers seem cost-effective for particular projects since you only pay for the work completed, eliminating the need for long-term commitments.
Cons:
- Limited Exposure to Ongoing Projects: Freelancers may not have experience with long-term, impactful projects. This leads to the lack of continuity and deep understanding of the organization that comes with full-time positions.
- Less Mentorship: Freelancers may receive less guidance compared to full-time employees, which can negatively affect their professional growth.
Tips:
- Use Freelance Platforms: Consider exploring Turing, Apollo Technical, and Candidate Labs platforms to find the best freelance AI developers. Also, don’t forget to outline project expectations and timelines for a successful collaboration clearly.
3. Recruiting Agencies
Turning to recruiting agencies that specialize in AI and ML roles can be a smart move for companies seeking seasoned professionals.
These agencies often employ recruiters with technical expertise, ensuring they understand the specific skills needed for AI positions. While the fees might be high, their motivation to succeed due to a performance-based payment structure can speed up the hiring process.
Having said that, it’s crucial to choose agencies with a proven history of success in AI recruitment to avoid being inundated with unqualified candidates.
Pros:
- Experienced Recruiters with Technical Expertise: AI-focused recruiting agencies usually have recruiters who understand technical aspects well. This means they better comprehend the skills and qualifications required for AI and ML positions.
- Motivated to Achieve Results: Since recruiting agencies often work on a commission basis, they are highly driven to produce results. This can lead to a quicker and more effective hiring process.
Cons:
- High Fees: Recruiting agencies typically charge 15-20% of the candidate’s first-year salary. While this guarantees quality candidates, it can be costly, especially for startups or organizations with limited budgets.
- Risk of Unqualified Candidates: There’s a chance that agencies may send unqualified candidates to meet their targets. Hence, it’s essential to carefully select agencies with a proven track record to avoid wasting time on the wrong candidates.
Tips:
- Choose Specialized Agencies: Look for recruiting agencies that specialize in AI and ML roles. Prioritize those with a history of successful placements in the AI industry to guarantee they understand the specific needs of these positions.
How to Effectively Hire AI Developers
Hiring AI developers might seem simple at first glance — just tell them what you need, and they deliver. But chances are things can go awry at any moment, and you might end up with not-so-ideal developers.
Below, we’ve broken the process into the smallest steps possible for the in-house recruitment approach.
Please note that you can consider skipping some steps here to suit your particular business situation.
Step 1. Define Job Requirements
To hire artificial intelligence engineers, you should start by clearly outlining the roles and responsibilities of the AI developer you need to hire.
It’s advisable to be as specific as possible about the skills, experience level, and minimum qualifications you require for the position.
Step 2. Source Candidates
Once you know what you’re looking for, it’s time to search for potential candidates.
Look on various platforms such as job boards, professional networks like LinkedIn, developer communities, and AI-related forums to attract suitable candidates.
Step 3. Conduct Initial Screening
Here, you should filter out resumes or portfolios of candidates who meet the basic requirements. This step can be done manually or solved automatically by resume parsers. Neurond’s Dr.Parsers brings the CV screening process to the next level by converting data from resumes. Recruiters can easily find the most matched engineer in the CV list, saving them a lot of time and effort in classifying qualified candidates.
The next step in sorting out suitable AI engineers is through preliminary interviews, either via video or phone calls, to evaluate candidates’ communication skills and suitability for the role.
Step 4. Technical Assessment
Candidates who pass the initial screening will now proceed to the technical round. This step involves a brief interview, taking around an hour to assess their qualifications.
Alongside the interview, you need to conduct technical assessments to gauge candidates’ proficiency in AI tools, programming languages like Python, and machine learning/ deep learning frameworks such as TensorFlow and PyTorch.
Remember to evaluate their experience in AI-specific areas like natural language processing (NLP), computer vision, deep learning, and neural networks. Also, presenting hypothetical AI-related scenarios or challenges can further test their problem-solving abilities.
Step 5. Review Past Project Experience
Assessing a candidate’s previous projects is one of the most important steps. If this is an important project of your company, make sure candidates have relevant experience. Then, you can reduce training time and avoid unexpected problems caused by inexperienced members.
To start by, ask them if they’ve worked on AI projects before and inquire about their role and achievements in those projects. The more detailed information you have, the higher your chance of finding suitable people for the team.
Step 6. Check Cultural Fit and Communication Skills
Apart from technical abilities, it’s vital to gauge whether candidates will fit well within your organization’s culture and values and if they can communicate effectively with your existing teams.
Regarding cultural fit, look at their values, work ethic, and communication style to see if they match your organization’s culture. An onsite interview will allow you to check their compatibility with your team and ability to thrive in your environment.
As for effective communication and collaboration skills, you should seek candidates who can express their ideas clearly, listen actively, and work efficiently in a team. And there would be a huge plus if they could simplify complex AI concepts for non-technical stakeholders to understand when collaborating.
Step 7. Onboard Your New AI Developer
Congratulations on finding your perfect AI engineer! Now it’s time for onboarding.
Bear in mind that you should include an office tour, completing HR documents, meeting with managers and executives, assigning training materials, and discussing company culture in your onboarding checklist.
Step 8. Set Expectations and Goals
Communicating your expectations and goals clearly to your new AI developer contributes to a successful onboarding process.
This entails sitting down with them to discuss the specific outcomes you’re looking for in their work, establishing clear milestones and benchmarks to track progress, and providing them with the necessary resources and support to succeed.
During this initial phase, building a foundation of trust and understanding is crucial. You should foster open communication and ensure your AI developer feels comfortable asking questions and seeking clarification.
Step 9. Integrate Them into the Existing Team
Now, it’s time to integrate your new developer’s AI capabilities into current teams.
Here, we suggest you promote collaboration across different departments and offer opportunities for knowledge exchange and personal growth.
You might also consider team-building activities or assigning a mentor to help your new AI developer navigate the organization more easily.
Step 10. Ongoing Training and Development for AI Developers
The field of AI is constantly evolving, so it makes perfect sense to provide ongoing training and development opportunities for your AI developers. They should be encouraged to stay updated on the latest AI advancements and attend relevant courses, workshops, and conferences.
Investing in the continuous growth of your AI developers not only enhances their skills but also keeps your organization ahead in AI innovation. So, please try to allocate resources for professional development and guide them toward the most relevant areas of improvement. By supporting their learning journey, you nurture a culture of continuous growth, setting your organization up for long-term success.
How Much Does It Cost to Hire AI Developers?
Glassdoor reported in February 2024 that the estimated annual pay for an AI developer in the U.S. with 1-3 years of experience is around $126K, expected to rise due to the growing demand for AI skills.
However, the expense to successfully hire AI engineers can vary based on several factors.
- Company Reputation
Well-known or respected AI firms may pay higher rates due to their reputation, while startups or less established companies might offer less when attracting talent.
- Location
The location of the company or developer plays a significant role in costs. Developers in areas with high living expenses may charge more, whereas outsourcing to countries with lower labor costs can provide cost-effective alternatives.
For example, hiring developers from countries like Vietnam may be more economical than hiring locally in the U.S. Vietnam is witnessing a remarkable surge in the embrace and advancement of artificial intelligence (AI). As a standout among the rapidly expanding economies in Southeast Asia, it is attracting a wave of investments and burgeoning interest from not only local startups but also global industry giants eager to explore its untapped capabilities.
- Developer Experience
Highly experienced AI developers with a proven track record generally demand higher salaries. Conversely, junior developers or those with less experience may be more affordable but require additional training and guidance.
- Project Complexity
The complexity and scale of AI projects also determine expenses. As you can tell, larger and more complicated projects often need greater expertise, leading to higher costs. Additionally, specific technologies, tools, or frameworks required for the project can increase the overall cost.
- Expertise and Team Size
The size and expertise of an AI team can cause the cost to vary, too.
AI projects often require a team of specialists with diverse skills, contributing to a higher amount of money. Moreover, specialized skills like natural language processing, computer vision, or deep learning may command higher rates.
Therefore, we suggest your company carefully assess your needs and budget constraints when hiring AI developers. Maintaining clear communication and establishing a well-defined scope of work are also essential for effectively managing costs throughout the project.
Partner with Dedicated AI Developers from Neurond AI Now!
Hiring AI developers requires careful planning, covering everything from setting project goals to evaluating technical skills and cultural fit. By following a structured approach with clear requirements, an effective engagement model, and a supportive environment for continuous learning, organizations can improve their chances of finding the right AI talent to propel their projects to success.
At Neurond AI, we offer top-notch artificial intelligence services to companies worldwide, utilizing various AI technologies, including machine learning, natural language processing, and computer vision. With over 15 years of unparalleled experience, we aim to be an integral part of your team rather than just a service provider. Rest assured, when partnering with us, you’ll get the highest quality and most impactful AI solutions tailored to your industry, enhancing automation, analysis, and efficiency.
Get in touch with us now for more details!