How To Hire for AI Roles (2025 Hiring Guide)
Gain insights and tips on how to hire the best AI engineers and other AI professionals who will bring value and improve business outcomes at your organization.

As more organizations adopt artificial intelligence (AI), disruption related to AI is reshaping the job market and hiring landscape. While some job tasks are being automated by AI to help increase productivity in existing roles, entirely new roles are also emerging to support AI initiatives.
Because AI is a rapidly evolving field, developing a strategy and process to fill AI roles requires thoughtful planning. Learn more about the growing demand for AI roles, along with steps and best practices for your organization to hire qualified AI professionals.
Table of contents:
- Demand for AI engineers is on the rise
- Benefits of hiring an AI developer
- How to hire for AI roles
- How to identify the best AI engineer or AI developer candidate
Demand for AI engineers is on the rise
Organizations are increasingly hiring AI professionals to help businesses effectively embrace and integrate AI technology. Data from the U.S. Bureau of Labor Statistics (BLS) predicts computer and information research science employment (which includes AI jobs) will increase by 23% between 2022 and 2032, compared to only 3% job growth across all occupations.
Additionally, data from the Upwork Research Institute shows that AI was the fastest-growing category on Upwork in the first half of 2023, with a 1,000% increase in generative AI job posts in Q2 2023 compared to the end of 2022. During the same period, searches related to generative AI on the Upwork platform increased 1,500%.
AI engineers typically focus on implementing and deploying AI systems at scale. An AI engineer’s core responsibilities include building and maintaining infrastructure, APIs, and data pipelines, as well as monitoring performance. This role is one of many seeing increased demand amid AI adoption. Other in-demand AI roles include data scientists, AI ethics consultants, AI product managers, natural language processing (NLP) developers, and prompt engineers.
Benefits of hiring an AI developer
While AI engineers are focused on implementing AI systems, AI developers design and build AI models and algorithms. Key responsibilities of AI developers include researching and selecting machine learning algorithms, training and testing AI models, and analyzing results.
One of the key differentiators between the two is that AI developers are involved in the earlier stages of AI projects, such as research, development, and training, while AI engineers oversee later stages. Depending on an AI professional’s skills and an organization’s specific needs, in some cases, AI developer and engineer roles may overlap.
Hiring an AI developer enables you to:
- Design and build custom AI models that align with your specific business needs to unlock new opportunities and gain a competitive advantage
- Automate routine and time-consuming tasks to increase productivity and empower team members to work more strategically
- Leverage AI-powered analytics to make improved data-based decisions, personalize customer interactions, and forecast future customer behavior and market trends
- Keep up with the latest AI technologies by engaging a proactive AI developer focused on continued learning and improvement
How to hire for AI roles
As organizations add new AI-focused roles, many are facing challenges with filling AI skills gaps. According to a survey of 800 non-executive knowledge workers and 800 C-suite executives conducted by edX, 87% of executives surveyed say they’re struggling to find talent with the AI skills they need. Following a thorough hiring process can help your organization source and hire qualified candidates for critical AI roles.
Steps in an effective hiring process include:
- Research the skills required for AI positions
- Create a job description with clear requirements
- Share AI roles on diverse sourcing channels
- Filter resumes for AI roles
- Prepare interview questions
- Review previous projects completed
- Check technical skills
- Evaluate soft skills
Research the skills required for AI positions
While some AI roles may require an advanced degree in computer science or machine learning, the field has evolved significantly in recent years; an individual’s degree isn’t necessarily as important as their specific skills. Successful AI professionals continue to take online courses and achieve certifications in various AI focus areas or complete projects to gain hands-on AI experience.
Before writing a job description or beginning your search to fill an AI role, research the top skills needed to succeed as an AI professional. Specific skills will vary depending on the role—such as an AI ethicist versus a machine learning engineer.
Here’s an overview of some of the most in-demand AI skills:
- Foundational technical skills such as proficiency in Python and other coding languages, machine learning algorithms, deep learning and neural networks, and natural language processing (NLP)
- Data science skills including data analysis, big data, and data engineering
- Machine learning engineering skills such as an understanding of building and deploying machine learning models and machine learning frameworks
- AI application skills including knowledge of chatbots, speech recognition, generative AI, automation, and AI-powered decision-making
- Soft skills such as critical thinking, communication, adaptability, and continuous learning
Create a job description with clear requirements
A job description includes an overview of the role’s responsibilities and requirements. An effective job description can help your team attract AI professionals who are a fit for your open roles and can be used to help evaluate candidates, by determining how their experience aligns with the requirements.
No matter the AI role you’re looking to fill, most job descriptions include many of the same elements, such as:
- Job title
- Employment type (full-time, part-time, or independent professional)
- A brief summary (including what type of work needs to be done, such as designing an AI solution from scratch or updating existing solutions by incorporating AI)
- A list of responsibilities
- An overview of required skills, characteristics, and qualifications
- Details about the company culture
- Desired hiring timeline
- Salary or budget
When looking to fill AI roles, your team can also leverage AI during the recruitment process to help craft job descriptions. For example, you can use a generative AI tool like ChatGPT, Google Bard, or Jasper and input a prompt requesting a job description for an AI engineer role. This approach can help draft an initial job description and your team can edit the draft to ensure accuracy and alignment with the role requirements.
Keep in mind, project descriptions for independent professionals differ from job descriptions for in-house workers. An effective description for a job post on Upwork often includes an anticipated timeline highlighting any milestones along the way, the budget (either hourly or fixed-price), an overview of deliverables, examples of similar projects, and a specific question or two for individuals to answer in their proposal.
Share AI roles on diverse sourcing channels
To reach qualified AI professionals, sharing your open roles where they’re searching for new opportunities is essential. If you post jobs only on your website or company social media page, for example, you’ll likely miss out on skilled talent searching for opportunities on other channels.
Sourcing channels may include:
- The company careers pages
- Traditional job boards
- AI and technology-specific job boards, such as AI Jobs, AI-jobs.net, Data Umbrella, thrive, and Tech Jobs for Good
- Job boards geared toward diverse populations including Diversity.com for underrepresented talent and Career Contessa for women
- Reddit channels, known as subreddits, geared towards AI jobs like, r/ai_ml_jobs
- Social media networks
- Referral programs
- Talent marketplaces like Upwork
Filter resumes for AI roles
Whether you’re vetting a prompt engineer, AI developer, AI ethicist, or other AI professional, effectively screening resumes is essential to identifying top AI candidates before moving on to the interview process and other hiring steps.
Criteria to take into consideration when you review resumes include:
- Work experience, including past roles and responsibilities
- Skills that align with the role requirements, such as machine learning, data science, natural language processing, and coding languages like Python
- Tools and frameworks relevant to AI roles including TensorFlow, PyTorch, and Spark
- Specific achievements and business outcomes, such as AI model accuracy improvement and data pipeline optimization
- Educational background, such as a degree in computer science, data science, or machine learning, or recent certifications relevant to the role
Because AI roles are in such high demand, manually reviewing a high volume of resumes can be overwhelming and time-consuming. AI can be used during this stage of the hiring process to automatically screen resumes, cover letters, job applications, and social media profiles.
Many applicant tracking systems (ATS), human resource information systems (HRIS), recruitment platforms, and talent management software solutions include AI-enabled features for this purpose. Leveraging AI for resume filtering can empower your team to spend less time on manual, routine tasks and focus more on building relationships with top AI candidates.
Prepare interview questions
The interview stage is one of the most critical parts of the hiring process. Effective interviewing can help your team get to know AI candidates on a personal level and better determine whether their skills and background are the right fit for your open roles.
Preparing a standardized set of interview questions for each AI role can help you stay organized and ask each candidate the same questions. Organizations also often leverage an interview scorecard to diminish bias and evaluate interviewees on the same scale. Many tracking systems and related solutions include integrated interview scorecards to help recruiting teams centralize all interview feedback.
Specific interview questions will vary from one AI position to another. Below, we’ve outlined some sample questions your team can use as a guide when developing your own interview questions for AI roles.
- Can you please share a bit about your background in AI?
- Which programming languages do you work in? Explain your experience in detail.
- Which AI tools or frameworks have you used on other projects? Describe your experience in detail.
- How does your AI experience align with this specific role and its requirements?
- What are some of the most interesting real-world applications of AI you’ve seen or experienced?
- What interests you most about AI?
- How do you stay up to date with the latest advancements in AI technology?
- Can you explain a technical challenge you’ve faced on an AI project and how you overcame it?
- How do you take ethical implications into consideration when working on AI projects?
- What do you see as some of the common misunderstandings about AI?
Similar to crafting job descriptions, recruitment teams and hiring managers can also use generative AI tools to research and develop job interview questions. Simply input a prompt, such as, “Write 10 interview questions to ask an AI engineer candidate.” Once you receive suggested questions, you can refine the prompt or edit and personalize the questions based on the role and your specific business needs.
Review previous projects completed
As part of the evaluation process, ask candidates to share examples of their most successful AI projects. Top job candidates often list their best projects and associated outcomes or include a link to their portfolios on their resumes, rather than simply listing responsibilities from their previous roles.
During the interview process, you can ask candidates to discuss the projects in more detail, such as what problem they were looking to solve, the steps they took to complete the project, how they incorporated feedback along the way, and any positive business outcomes as a result of the project. A common way to evaluate projects is by using the STAR method, which takes the situation, task, actions, and results into consideration.
Examples of AI projects may include:
- Developing an AI-powered chatbot to improve customer service
- Leveraging deep learning and neural networks to streamline document management for a law firm, health care network, or other client with a high volume of documents
- Implementing AI-powered analytics to make data-based decisions and increase customer engagement
Many developers and other technical professionals upload their work, code, projects, and datasets to Github, a code hosting platform. Your team can review candidates’ Github profiles to verify projects and contributions highlighted during the interview process. If you’re considering engaging independent professionals for AI projects, Upwork profiles feature each individual’s work history, completed jobs, client ratings and reviews, and portfolio projects, which simplifies the process of verifying individuals’ past experience.
Check technical skills
No matter the open position, a candidate’s resume, interview responses, and other information shared during the hiring process can help recruitment teams and hiring managers gauge how an individual may perform in a role. In addition, incorporating technical skills testing into the hiring process can help your team have more confidence in a candidate’s abilities by seeing their skills in action. This is particularly relevant for a complex technical role like an AI engineer or AI developer.
Talent assessment tools are available to help simplify the process of testing for technical skills by automatically sending assessments to candidates and automatically scoring and ranking candidates once testing is complete.
Some talent assessment tools are designed specifically with AI and other technical roles in mind, such as:
- Codility. As a technical interview platform, Codility enables organizations to test coding and other technical skills and make data-based hiring decisions for developer, engineer, and related roles while mitigating bias. Key features include role-specific assessments, coding challenges to test candidates’ skills, and live technical interviews and assessments using templates and whiteboarding tools. Given the rise of generative AI, Codility launched an assessment specific to AI-assisted engineering skills in 2023.
- Filtered. The Filtered AI-powered platform helps talent acquisition teams and engineering hiring managers evaluate technical skills for full-stack engineering, QA, data science, blockchain, DevOps, and other technical roles. Testing options include coding challenges, interactive video interviews, and role-specific technical assessments. In 2023, Filtered introduced a prompt engineering job simulation assessment to test candidates’ ability to write prompts for large language models (LLMs).
- Qualified. Built by developers and for developers, Qualified helps technology companies and technical recruiters assess software engineers and other technical professionals at scale. A built-in skills assessment library features tests focused on more than two dozen common coding languages used by developers, including Python, a common language used in AI. Capabilities include coding challenges, benchmarks to understand individuals’ strengths and weaknesses, and detailed feedback on code quality.
Evaluate soft skills
In addition to testing technical skills, evaluating soft skills can help determine an individual’s ability to work effectively on a team. Soft skills can be more difficult to measure than hard, technical skills, but are just as important to success in any role.
Because soft skills are human traits and characteristics, they continue to be essential in the age of increased AI adoption. According to a Visier survey of 1,000 employees, 58% of respondents believe AI can’t replace soft skills, the highest percentage across all categories of skills.
Here are some of the most important soft skills to evaluate:
- Communication
- Collaboration
- Teamwork
- Critical thinking
- Problem-solving
- Time management
- Creativity
- Motivation
Soft skills can be measured during the interview process by asking situational questions during the interview process, leveraging a talent assessment tool to assess how candidates may respond to certain scenarios, or asking situational questions during the interview process.
Talent assessment tools that focus on soft skills include Caliper Profile, DiSC, Plum, and Pymetrics.
Here are some examples of interview questions that can help you learn more about a candidate’s soft skills:
- How would you approach developing new skills or learning about a new focus area in AI?
- What are your preferred communication channels and style?
- What steps would you take to prepare for a team brainstorming session?
- How would you handle giving a team member constructive feedback?
- What’s your process for managing different projects and priorities on a day-to-day basis?
How to identify the best AI engineer or AI developer candidate
Once you complete relevant hiring steps, a few top candidates will likely remain in the final hiring stage. Review the initial requirements outlined in the job description and align with your team to determine the best-fit candidate for your AI engineer, AI developer, or other AI role.
Similar to interview scorecards, many recruitment platforms include AI-powered algorithms or scoring systems to evaluate each candidate throughout key steps in the hiring process. Scoring systems often include a simple scale, such as zero to five stars. The total average score across all steps helps recruitment teams identify the best AI candidate for each role.
Your team can review candidates with top scores and discuss how their qualifications and experience align with the role expectations. While a scoring system can help streamline the process, the final hiring decision should ultimately be made by a human, not simply based on the score. In fact, a survey conducted by ResumeBuilder of 1,000 employees involved in hiring found that 85% of respondents believe that AI can help provide recommendations on candidates but final hiring decisions should be left to people.
Once you decide on the top candidate, move forward with extending an offer as soon as possible. Because AI skills are in such high demand, the most qualified AI professionals are often juggling multiple opportunities and receive job offers soon after starting their search.
Engage skilled AI professionals
AI-related roles will likely continue to see increased demand across industries and businesses of all sizes. Engaging qualified AI talent can help your business drive efficiencies, make improved data-based decisions, and gain a competitive advantage, among other benefits.
Whether your team is looking to fill AI skills gaps with an AI engineer, AI developer, or any other AI-focused professional, your team can find the talent you need on Upwork. Skilled independent professionals with a diverse range of AI capabilities are available on Upwork to help your organization embrace the latest AI technologies and drive positive business outcomes, while saving time and reducing operational costs.
Learn more and start searching for experienced AI professionals today.
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