How to Evaluate Hiring an AI Engineer at Your Company (2025 Guide)
According to the 2025 LinkedIn Jobs on the Rise report, artificial intelligence engineers hold the fastest-growing job title in the U.S.
The recent AI surge has increased the number of new AI startups and existing companies exploring these technologies.
But to hire an AI engineer for your organization, you need to know what qualification and skills to look for and how to asses their technical expertise. After all, this job title can be incredibly complex and advanced.
On top of that, competition for top engineer talent is intense.
In this article, you’ll learn everything you need to know to evaluate AI engineer candidates, including how to interview them with the right questions and score their education, skills, and experience.
What Is an AI Engineer?
An AI engineer is a professional who designs, develops, and implements AI models and systems. They possess a blend of skills in various technical fields like programming, data science, and machine learning. These technologies and skills enable them to create solutions that mimic human intelligence.
The AI engineering roles often involve tasks such as natural language processing, computer vision, and predictive analytics.
However, not all AI engineers build models from scratch. Some focus on applying existing models, such as large language models like GPT, by turning them into APIs or embedding them into real-world applications.
Types of AI Engineers
The scope and role of AI engineers vary from job to job. An AI engineer can have a multi-faceted role in your company. However, AI engineering, as a profession, can be roughly categorized by the following types:
1. Machine Learning Engineer
Machine learning (ML) engineers develop and deploy algorithms that enable machines to learn from data and make informed decisions without explicit programming. They work on projects such as recommendation systems, predictive analytics, and fraud detection.
This role requires strong programming skills, knowledge of frameworks like TensorFlow and PyTorch, and expertise in data structures.
P.S. These engineers are also rare and hard to recruit. If you’re struggling to find qualified candidates, our list of Top 12 Machine Learning Recruiters can connect you with experts who specialize in sourcing top-tier ML talent.
2. Deep Learning Engineer
Deep learning engineers specialize in neural networks and advanced algorithms to solve complex problems. They design deep learning architectures, train models on large datasets, and optimize models for performance and scalability.
3. Data Engineers
Although not entirely an AI engineering profession, data engineering overlaps with data and AI. Data engineers design and maintain the infrastructure for data collection, storage, and analysis. They make sure that the data that’s fed into AI models is the most relevant, accurate, and high-quality.
According to the CIO, a data engineer is the most in-demand tech job in 2025.
4. Engineers Working with AI
These engineers don’t create AI models but work with them for their jobs. This is the role most organizations would be looking to fill, as they turn to AI to improve their products and services. Think of all the SaaS solutions that incorporate GPT models from OpenAI into their products. They do so with the help of software engineers working with AI.
This role doesn’t require a deep understanding of machine learning as such, but it does necessitate some fundamental knowledge of how these models work and how they may be deployed.
What are the Roles and Responsibilities to Look for When Hiring an AI Engineer?
When looking for an AI engineer for your company, it’s important to define the expected roles and responsibilities. The exact responsibilities depend on the type of AI engineer you need and the projects that are in the pipeline.
Here are the core responsibilities of most AI engineer roles:
Developing AI models and algorithms: Design and build machine learning models to solve complex business problems. This involves selecting appropriate algorithms, training models on large datasets, and fine-tuning them for optimal performance.
Integrating AI solutions into existing systems: They work closely with software developers and IT teams to embed AI functionalities into current applications.
Collaborating with cross-functional teams: AI engineers often collaborate with data scientists, product managers, and other stakeholders to align AI initiatives with strategic goals and user expectations.
Maintaining and updating AI systems: Post-deployment, they monitor AI systems for performance, making necessary adjustments and updates to improve efficiency and accuracy.
Ensuring data privacy and compliance: AI engineers implement safeguards to protect sensitive data and ensure that both model training and deployed applications meet regulatory and ethical standards.
“One may expect Google engineering to largely consist of implementing PhD level algorithms, and while that's sometimes true, much of a search or AI engineer's job involves looking at examples, spotting patterns, hand labelling data, and other non-scalable, in-the-weeds analysis,” shares Shreyans Bhansali, Co-Founder, Maven.
Qualifications Required for an AI Engineer Role
The very first thing most employers look for when screening AI engineer candidates is their professional qualification. As this job is highly technical, a solid educational and technical background proves that they have the foundation covered.
Most AI engineers hold at least a bachelor's degree in computer science, data science, software engineering, or a related field.
These programs typically cover essential topics such as programming languages, data structures, algorithms, and introductory machine learning concepts. For instance, a bachelor's degree in computer science provides a strong understanding of the software development process and data analysis, which are part of most AI projects.
Some professionals persue a master's degree in artificial intelligence, machine learning, or data science. And that’s what many employers prefer. According to a review of job posts by the National University, three out of four companies favor a candidate with a master’s degree when filling AI roles.
That’s because advanced degrees can offer deeper insights into complex AI topics and may be preferred for research-intensive roles.
That said, becoming an AI engineer without a traditional college degree is possible. Thanks to alternative learning routes, such as online courses, bootcamps, and self-study, individuals can get on the career pathway of an AI engineer. For example, online learning platforms offer specialized programs in AI and machine learning that can help build a strong foundation.
According to Coursera, while many companies require at least a bachelor's degree for entry-level jobs, it's quite common for individuals to use professional certificate programs to learn the skills they need and prepare for interviews.
Pro Tip: When evaluating AI engineer candidates, focus more on skills and experience than professional qualifications to increase your talent pool. Companies like IBM and Google, which are heavily invested in AI technologies, have removed the minimum bachelor's degree requirement for many roles.
Key Skills Required for an AI Engineer Role
Here are the typical skills you should assess when hiring an AI engineer at your organization: .
Programming languages: First off, they need to have experience with programming languages. For AI work, Python, R, and Julia are the big ones. Python is the industry favorite, according to DataCamp.
Machine learning frameworks: They should know their way around tools like TensorFlow, PyTorch, or Scikit-learn. These help build and train models that can actually learn from data and make predictions, which is the whole point of AI.
Math and stats (analytical skills): A strong grip on things like linear algebra, calculus, probability, and statistical analysis is key. This stuff helps them understand how AI algorithms tick and lets them build smarter, more reliable models.
Data handling: For roles focused more on the data side, they’ll need experience managing big datasets, cleaning and prepping data, and using databases. Knowing tools like SQL or data warehousing platforms is a big plus.
Strong problem-solving skills: Great AI engineers are also sharp thinkers. They should be good at breaking down complex problems and coming up with smart, creative solutions that actually work in the real world.
Soft skills: Last but definitely not least, soft skills like interpersonal skills, active listening skills, and decision-making skills matter a lot. These folks often work on teams with people from all sorts of backgrounds. So being able to clearly explain their ideas, work well with others, and roll with the punches makes a huge difference.
“75 percent of long-term job success depends on people skills, while only 25 percent on technical knowledge,” says Peggy Klaus, Author of The Hard Truth About Soft Skills.
How Much Is an AI Engineer's Salary?
According to Glassdoor 2025 data, the median pay of an AI engineer in the US is $135,000 annually. This is above the median pay of several ICT professions, including programmers, network architects, and system analysts, according to the 2024 US Bureau of Labor Statistics data. Thus, AI engineering is one of the highest-paying jobs in the tech industry.
As an employer, you should know and understand the average AI engineer salary, so you can offer competitive compensation according to industry trends or an even better package to attract the best talent. This is all the more relevant at a time when there’s an AI talent shortage.
To attract top talent in AI engineering, it’s best to go over rather than under the median salary. The compensation should be even higher for more technical roles like ML engineers. Such roles are already challenging to fill with limited experts in the job market.
Alternatively, you can set a pay that’s commensurate with the skills level and experience. For example, an AI engineer with 5+ years of experience could be offered a salary of over $200,000.
How to Find AI Engineers for Your Company?
There are several avenues for your organization to source AI engineers. You could start looking for one by creating a job post on hiring platforms like Indeed or Monster.
However, there are other, more streamlined options as well that may help you hire an AI engineer quickly and at a lower cost.
1. Engage a Recruiting Agency
Partner with a specialized recruiting agency for the hiring process. These agencies have access to a broad talent network and can provide interview-ready candidates who match your specific job requirements. This eliminates the need to go through hundreds of applications and screen candidates.
Moreover, this innovative approach is best for AI startups that may not have the resources or expertise of a dedicated HR department. Enterprises can benefit too. Their recruitment teams can collaborate with specialized agencies to source candidates and manage the initial interview phase, saving valuable time and effort.
P.S. Looking for vetted professionals? Explore the Top AI Recruiters, Staffing Agencies, & Headhunters to connect with the right talent.
2. Source Directly from Universities and AI Bootcamps
Another option for hiring AI engineers, especially if you’re looking for fresh talent hot off the presses, is universities and bootcamps.
Many institutions offer programs focused on AI and ML, producing graduates with the necessary technical skills. If you don’t necessarily need people with practical experience, this is a good choice to tap young, fresh minds who are eager to learn.
Here are the universities in the US with the best AI programs, according to US News & World Report:
Carnegie Mellon University
Massachusetts Institute of Technology
Stanford University
University of California, Berkeley
University of Illinois, Urbana-Champaign
3. Try Freelancer Marketplaces
Freelancer platforms like Upwork provide access to a wide range of AI engineers who may be available for contract work or one-off projects (freelancers are better suited for the latter). These platforms allow you to review tech candidate profiles, assess their track record, and determine their suitability for your projects.
Pro tip: When looking for AI engineering talent yourself, turn to AI-powered sourcing to supercharge your screening process. AI tools can help identify passive talent, improve candidate engagement, and streamline the entire hiring process.
How to Evaluate Hiring of an AI Engineer: Step-by-Step Guide
It's essential to follow a structured AI engineer hiring process to ensure you attract and retain top talent. This step-by-step guide outlines the key stages in hiring an AI engineer, incorporating best practices and considerations relevant to your company's needs.
1. Define the Scope and Responsibilities of the Role
First off, clearly outline the specific tasks and responsibilities the AI engineer will undertake. Determine whether the role focuses on developing ML models, implementing NLP solutions, or integrating AI into existing systems.
Clear roles and responsibilities help attract candidates whose skills align with your project requirements. More importantly, this clarity helps you screen applicants and eventually score selected individuals fairly.
2. Source Candidates (Directly or with the Help of a Recruiter)
Fortunately, you have multiple channels to identify potential candidates for the AI engineer position. This includes job boards, professional networks, and recruitment agencies specializing in AI roles.
Determine which approach best suits your organizational needs and capacities.
If you have a well-established recruitment team, you may source candidates on your own. Otherwise, recruitment agencies can step in to match the job with a properly vetted, qualified professional (that obviously speeds things up).
3. Screen and Shortlist Potential Hires
Implement a screening process that evaluates both technical skills and culture fit.
You can use an applicant tracking system (ATS) or another hiring tool, preferably with AI features, to quickly scan resumes and cover letters to find candidates best suited for roles.
Some human oversight is recommended during this process, so you don’t miss out on potentially good candidates. AI hiring tools can be biased, but there are ways to overcome the AI hiring bias issue.
4. Conduct Interviews
Structure interviews to explore the candidate's professional experience and approach to AI projects.
Include technical interviews that assess knowledge in areas like deep learning frameworks, neural networks, and language modeling.
But don’t forget those crucial soft skills either.
Behavioral interviews can help determine how candidates handle challenges and work within a team. AI teams often work in challenging environments with complex problems to solve. Include scenario-based questions to evaluate their problem-solving strategies (sample questions listed later).
P.S. You can also use this guide on how to interview AI talent like a pro.
5. Assess Skills, Expertise, and Experience
Review the candidate's track record, including previous projects and contributions to AI initiatives. Examine their familiarity with a variety of tools and programming languages relevant to your projects.
Interviews can only go so far to give you an idea of their knowledge, but you need to put it to the test to ensure they have the technical expertise to excel at their job.
Technical Assessment
Consider their experience with tasks such as developing machine learning models or enhancing user experiences through high-quality AI solutions. One way to do that is through technical assessment tests, for example, code tests or take-home assignments.
Ensure that the content of the actual assessment process relates to the projects they’ll be working on.
Also, align them with the expected level of theoretical knowledge and expertise. In other words, the test can be basic if you’re looking to fill an entry-level AI engineer job.
You can use testing tools like Test Gorilla to conduct technical assessments specific to the skills and technologies used in the crucial role of AI engineer. There are tests on ML, frameworks like PyTorch, and DL.
Interview Rubric or Scorecard
Use a rubric or scoring card to evaluate candidates during interviews and any tests.
Having a rubric:
Standardizes candidate evaluation across different interviewers
Reduce unconscious bias
Focus on must-have vs. nice-to-have skills
Identify strengths and development areas objectively
Again, make sure to include both technical and behavioral competencies in the rubric you design.
Assign weights according to your team's priorities (e.g., if deploying models is more important than research, increase the weight for system design).
Here’s a sample rubric for an AI engineer job:
| Competency Area | Criteria | Weight (%) | Rating (1–5) | Notes |
|---|---|---|---|---|
| Programming Skills | Proficiency in Python, R, or C++. Familiarity with NumPy, Pandas, etc. | 15% | ||
| ML Frameworks | Experience with TensorFlow, PyTorch, Scikit-learn | 15% | ||
| Mathematics & Theory | Solid understanding of linear algebra, calculus, statistics, and ML theory | 15% | ||
| Data Engineering | Ability to clean, transform, and pipeline data; SQL/NoSQL knowledge | 10% | ||
| Problem Solving | Demonstrated ability to approach ambiguous problems logically | 15% | ||
| System Design | Understanding of model deployment, scaling, and production readiness | 10% | ||
| Communication | Clarity in explaining technical ideas to technical and non-technical teams | 10% | ||
| Culture Fit | Collaboration, initiative, continuous learning, adaptability, and values alignment | 10% |
6. Onboard the Winning Candidate
Once a candidate is selected, ensure a smooth onboarding process. Provide clear documentation of project requirements and company culture. Assign a mentor or point of contact to assist with the transition into the team.
Use task management tools to outline initial tasks and expectations. This way, you can ensure a structured integration into your software development teams.
AI Engineer Interview Questions
Below is a curated list of interview questions your hiring managers or team leads can use to evaluate AI engineer candidates. These questions are designed for companies developing AI-powered products or integrating AI into existing business processes.
Technical expertise
What machine learning model have you built and deployed in a production environment? Walk us through your process.
How do you decide between using a rule-based model versus a deep learning model for a project?
Describe a project where you used natural language processing. What tools and frameworks did you use?
How do you handle overfitting in your machine learning models?
What experience do you have with neural networks? Can you explain how backpropagation works?
Which programming languages are you most comfortable with when developing AI applications and why?
Tools and frameworks
Have you worked with TensorFlow, PyTorch, or Scikit-learn? Give examples of how you've used these in real-world projects.
What’s your approach to managing large datasets for training machine learning algorithms?
Describe your experience with model versioning and deployment pipelines. What tools did you use?
Problem-solving and critical thinking
Tell us about a time you had to debug a failing AI model. How did you identify and resolve the issue?
How do you evaluate the AI model performance beyond accuracy metrics?
Collaboration and communication
How do you ensure effective collaboration with cross-functional teams, such as product managers and backend developers?
Share an experience where you had to explain a complex but key concept to a non-technical stakeholder.
Adaptability and continuous learning
AI is a fast-changing field. How do you stay up to date with the latest research and tools?
What has been your most significant learning experience from a failed AI project?
System design and scalability
How would you design an end-to-end pipeline for deploying a machine learning model in production?
What strategies do you use to ensure your AI systems scale efficiently with increasing data volume or user load?
How do you monitor AI models in production to detect performance drift?
Ethics and responsible AI
How do you approach fairness and bias in AI models?
Have you worked on any projects where ethical considerations impacted design decisions?
What steps do you take to ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) in AI development?
Source the Best AI Engineers
The pressure to integrate AI talent can lead to rushed decisions or costly mistakes. By breaking down the evaluation process, defining your needs, vetting skills, and aligning expectations, you eliminate uncertainty and reduce risk.
Once you’ve clarified the role and its real impact, hiring a skilled engineer becomes a strategic step, not just a trendy one.
Keep in mind: competition for top AI talent is intense. Therefore, you can partner with specialized AI recruiting firms to get early access to highly qualified candidates and streamline your search. Use the interview questions and technical assessment guide above to make confident, informed hiring decisions.
For large-scale AI initiatives, consider working with top AI consulting firms to create a structured hiring plan and access high-caliber talent aligned with your project goals.
FAQs
How do you hire an AI engineer?
Hiring an AI engineer involves a step-by-step process:
Define the role: Clearly outline the job requirements, including specific tasks and responsibilities.
Source candidates: Use job boards, recruitment agencies, and your talent network to find qualified candidates.
Screen applicants: Assess technical skills, soft skills, and culture fit through coding assessments and interviews.
Conduct interviews: Include technical interviews focusing on areas like machine learning, natural language processing, and programming languages.
Onboard the candidate: Provide clear documentation of project goals and integrate them into your engineering teams.
How do you evaluate an AI engineer?
Evaluation of an AI engineer should focus on both technical and soft skills:
Technical skills: Strong proficiency in programming languages (e.g., Python, Java), experience with machine learning frameworks (e.g., TensorFlow, PyTorch), and understanding of data management.
Soft skills: Teamwork skills, effective communication, and adaptability are crucial for collaboration within your company culture.
Track record: Review past projects and contributions to assess their experience and success in similar roles.
How much does it cost to hire an AI engineer?
Salaries for AI engineers vary based on experience and location. In the US, the median salary of AI engineers in 2025 is $135,000 per year. However, experienced AI engineers may cost much more than when you factor in the higher pay grade and benefits like paid time off and health insurance. According to Built, the total compensation can reach around $210,595 per year.
What does an AI engineer need to know?
An AI engineer should have proficiency in programming languages like Python and R and have knowledge and expertise in AI frameworks. They should also have a strong foundation in computing technology, like a bachelor’s degree in Computer Science and/or AI-related certifications.
Which companies hire AI engineers?
Companies working on or with AI technologies typically need the expertise of AI engineers. Big tech giants like OpenAI, NVIDIA, Google, and Meta use AI engineers for their AI projects. But startups are also hiring AI engineers to create innovative solutions, sometimes built upon the AI from tech giants.
What is the difference between an AI engineer and an AI developer?
While both AI engineers and developers work with artificial intelligence, their focuses differ:
AI Developer: Specializes in designing and building AI models and deep learning algorithms, focusing on the crucial aspect of development.
AI Engineer: Focuses on implementing and deploying AI systems at scale, integrating models into existing software frameworks.
What AI engineering skills are most in demand?
Some of the most in-demand AI skills include machine learning, natural language processing, data science, robotics, and cybersecurity. These skill sets are popular in AI development roles, where AI engineers create, train, and maintain models or incorporate them into systems and applications.
Which AI engineer has the highest salary?
Salaries can vary widely, but senior AI engineers with extensive experience and specialized expertise, such as expertise in deep learning and neural networks, command the highest salaries. Their annual salary can easily be above $200,000.
Can an AI engineer work from home?
Yes, many companies offer remote work options for AI engineers. The nature of AI engineering tasks, such as coding and data analysis, allows for flexibility in work location. However, collaboration with internal tech teams and participation in meetings may require coordination across different time zones.
Also, the performance requirements for building and training AI systems may exceed the capabilities of standard personal computers, making access to high-performance infrastructure essential.