7 Skills to Look for When Hiring an AI Engineer

Having an AI engineer working on the development of an AI solution can mean the difference between bringing to life a project that provides real business value versus spending resources on developing a prototype which will remain unfinished. 

On the other hand, there are many different tools, technologies, and frameworks connected to AI emerging constantly, making the choice of the right one hard. Hiring an AI engineer would require knowing the difference between all these aspects.

Think about interviewing two candidates. The thing is that both have these words in their CVs – “Python”, “machine learning”, and “generative AI”. One of them will provide you with a cool demo. 

Another one – with detailed explanations about integration of the AI model into your current infrastructure, its precision, protection of sensitive data, and proper maintenance after implementation. Whom will you pick for further work on your project?

It all depends on what the objectives of your business are, but knowing just technical things alone would be insufficient.

The AI Engineer is the one who knows how to code and has knowledge in AI solutions and problem solving.

Following are 7 skills of an AI engineer which should be considered important for your company, along with the questions that you can ask during interviews. These skills also provide a useful framework for hiring an AI engineer who can deliver practical business results.

What Does an AI Engineer Do?

AI engineers are responsible for designing and developing applications using artificial intelligence in order to solve actual business tasks. This depends on the nature of your project. It may require development of predictive models, generative AI applications, language models integration and intelligent automation.

For instance, you could be required to use a chatbot, recommendation engine, document automation software or any other type of application which can help your employees to search for data within the documents of your organization.

AI Engineer vs. Machine Learning Engineer vs. Data Scientist

These jobs have similarities but different main responsibilities.

AI engineer: Mainly engaged in creating applications based on AI technology.

Machine learning engineer: Mainly involved in developing, optimization, deployment and support of machine learning models.

Data scientist: Mainly concerned with data analysis, experiments, insights and creation of prediction models.

It is up to you to select the most appropriate option depending on the nature of your project.

If you are designing an AI application, then the ability to develop and integrate applications will be more important. Otherwise, you might need deeper knowledge of machine learning.

After you define the job, there are seven skills which can help you identify the right person for the job. Having clear expectations at this stage makes hiring an AI engineer more effective.

1. Strong Programming and Software Engineering Skills

Programming is another key skill that needs to be considered while hiring an AI engineer. AI models don’t usually work standalone; they require integrations with applications, databases, APIs, and other types of systems to become useful.

An engineer who has good knowledge of AI but finds it hard to develop quality software programs might find it challenging to convert a promising idea into a stable business solution. This is why programming ability should remain a priority when hiring an AI engineer.

Programming Skills – Python and Other Languages

The reason why Python is popular among AI developers is the availability of numerous libraries for machine learning, data processing, and deep learning.

Candidates who are able to collaborate well with software like NumPy, pandas, scikit-learn, PyTorch, and TensorFlow (where appropriate) are the desired ones.Of course, it will definitely be an advantage if the candidates have knowledge of the SQL language, particularly in cases where structured data are required.

Writing Clean, Maintainable Code

Many AI initiatives may develop according to the changes in business needs. The programmer should know how to write code which is easy to read, test, extend and maintain by other developers.

Check for experience with:

• Modularity and code reuse.

• Unit testing and integration testing.

• Version control with Git.

• Debugging and exception handling.

• API development and software integration.

• Documentation and dependency management.

Such practices will prevent your company from accumulating technical debt.

Understanding System Design and Integration

The model can work well standalone, but not necessarily within the system in which it will be used. Competent candidates have an idea how to develop the software around AI models, handle possible errors, process requests and integrate the AI functionality.

How to test this quality: Set a candidate a simple programming exercise with an API or an AI-enabled service. Make sure that he/she writes clean code, handles invalid input data, writes tests and explains his/her approach.

Interview question: How do you transform a working AI notebook prototype into a maintainable service, available to other engineering teams?

2. Deep Understanding of Machine Learning Fundamentals

While the AI platforms can assist you with testing the model, it is also important to have a good understanding of machine learning. An AI engineer should have knowledge of how models learn, how to measure performance and why sometimes they can give the wrong output.

In such a way you can avoid wasting resources on ‘successful’ solutions that are not actually successful. For companies which are looking for an AI specialist to hire, assessment of this understanding will help to separate real professionals from the others.

Knowledge of Core Machine Learning Concepts

Look for someone who has knowledge of supervised and unsupervised learning, along with classification, regression, clustering, neural networks and deep learning.

They should be able to understand what training, validation and test data sets are. They should also understand issues such as overfitting, underfitting and data leakage.

Depending on what the role is, they might not need to have the same level of knowledge of training models as someone who is embedding pre-existing foundation models.

Statistics and Model Evaluation

They understand what metric to use to evaluate the model

For instance, if your data is imbalanced then just looking at accuracy might not tell you the whole story. The model might have a high accuracy but fail to detect enough fraudulent transactions.

Look out for their knowledge of metrics like:

• Precision – how well does the model predict positives?

• Recall – how many positives does the model find?

• F1 Score – how well does the model balance the two?

• Confusion Matrix – where are the true and false classifications?

The candidate should understand which metrics are important for the use case and why.

Understanding model limitations

Any seasoned AI engineer is aware of the fact that there might be situations where the most accurate model isn’t the one they’d choose to go with. This could happen because of reasons related to computation time, time to get results, interpretability of the model or consequences of wrong predictions.

To find out about this, you can ask them about the model they’ve used, metrics they’ve applied and how they analyzed anomalies.

Interview Question:

You have a model with 95% accuracy, but still have too many predictions being wrong. What would you check then?

This question should make them talk about analyzing the quality of data, potential class imbalance, the way the model is evaluated, error patterns and whether the chosen metric reflects the business objectives.

3. Expertise in Generative AI, Large Language Models, and Prompt Engineering

With companies exploring Generative AI, it’s a valuable skill to be able to create useful applications using large language models. As a result, generative AI expertise has become an important consideration when hiring an AI engineer.

But knowing how to use a model API is only one aspect of the role. The engineer will need to know how to apply context, structure outputs and assess the responses provided while understanding the limitations of generative AI.

Understanding of Large Language Models

Look for an understanding of terms like tokens, context window, inference, structured outputs, model API.

Candidates should have an understanding that language models are generating responses based on patterns they have learnt and the information available to them. This means there can be inaccuracies, inconsistency and lack of reliable evidence to back up the results.

Candidates should be able to compare hosted models with open weight models based on performance, cost, privacy and deployment.

Prompt Engineering and Context Management

Prompts that are designed effectively require you to define tasks, give right information, specify formats for your outputs and try out variations.

For instance, when using an AI assistant to extract information from invoices, you would like to make sure that you have good output formats, guidelines on how to deal with any missing fields and validate the data that comes out so that any wrong outputs are filtered out from your system.

Choose candidates who will experiment with your prompts.

Applications and AI Agents for RAG

There are two great ideas when designing modern AI applications: Retrieval Augmented Generation (RAG) and AI agents.

With the help of RAG, your application will be able to find related information in an external source and send it to the language model. This way your AI assistant will be able to answer questions based on company documents, product information or internal policy.

As for AI agents, they will use tools, access certain information or take actions as part of some workflow. Since agents take actions, they require proper authorization, testing and security measures.

Choosing the Right AI Approach

In some cases, depending on the kind of problem and requirements that you have, it may be more beneficial for you to make use of a simple API of models, optimize the search algorithms, improve the small model or even resort to regular software rather than designing an agent.

How do you evaluate this? Ask them to design an AI assistant that utilizes documents and see how they will control hallucinations, manage dynamic data, and evaluate the output.

Interview Question:

How would you design an AI assistant that answers questions from internal documents of the company without causing hallucinations?

4. Data Engineering, Data Quality, and Retrieval Skills

Another thing to keep in mind is the fact that good outcomes cannot be obtained if your data is incomplete, outdated or even irrelevant despite having an effective AI model.

He/She should be aware of the way that data flows through a system, the way that it can be prepared and how to get the right data. Assessing these abilities is another important part of hiring an AI engineer, particularly for businesses working with large amounts of internal information.

Data Preparation & Pipeline Development

An AI engineer is bound to work on different types of data such as from structured databases, documents, logs, images and so forth. He/She must thus possess the knowledge about the way to prepare and structure data for their applications.

Seek candidates who have worked with SQL, data cleaning, handling missing data, deduplication and building scalable data pipelines.

Another very important thing is validation. Even if you have built a good model, you may not get the right results due to the fact that your data is outdated.

Embeddings, Vector Search, and Retrieval Quality

The way that the embedding works is vital at this point. They need to know how the embeddings would tell them to fragment the document, filter by metadata, indexing, and how to find relevant text.

If a company were to build their knowledge assistant and not find the right policy document then the language model will give a confident answer based on irrelevant information. An experienced engineer will look at the retrieval process and see if there are ways to improve. This could include improving the preparation, splitting, metadata, reranking or access to documents.

Data Privacy & Access Control

Many AI applications will touch on sensitive business data. The engineer should understand access control, retention of data, how to handle data securely and what could happen if they were to expose documents that shouldn’t be seen.

When retrieving, you must make sure that the access is limited to the correct permissions. Just because an AI system can find it doesn’t mean you can give it to an employee.

How to test this skill: Give a small amount or imperfect documents and ask the candidate to create a retrieval process. Ask them to explain how they would find irrelevant documents and make sure that the responses do not include any unauthorised information.

Interview Question: Your RAG application is returning incorrect answers. The language model is powerful enough. How would you find out if it is the retrieval or generation?

Find out the method that they will use in checking the quality of their data as well as the process of their retrieving and debugging.

5. Model Deployment, MLOps, and Cloud Infrastructure

Having a working prototype is only the first step. It’s important that you’re able to get a system up and running that can scale.

It’s good practice as an AI engineer to know how to deploy AI systems, monitor them, make changes, and ensure they perform after deployment. This is especially important if you’re adding AI functionality into your critical products. When hiring an AI engineer, production experience should therefore be considered alongside development skills.

Deployment of AI Models

You’ll want to look for someone who has experience deploying models using API’s, inference services or suitable application architecture.

The candidate should have knowledge of how to handle dependencies, setup environments, deal with errors, and integrate AI services.

Tools such as Docker can be used to help deploy consistent applications. Depending on your infrastructure, you’ll need different technologies.

Cloud Platforms

Being able to have knowledge on cloud platforms, for example, AWS, Microsoft Azure or Google Cloud, may be advantageous.

Rather than having someone who knows everything about the different providers, it is best to have someone who understands the concepts.

Monitoring, CI/CD, and Model Lifecycle Management

It is important that AI can be monitored over time.

Look for candidates that have knowledge of:

• Automated testing/deployment pipelines

• Version control of code/models/prompts

• Latency, uptime, error and cost monitoring

• Model quality and relevant data monitoring

• Roll back procedures and incident handling

Tools like MLflow can help with experimentation, model management, deployment and evaluating AI apps.

Reliability, Speed & Cost

Engineers should be able to identify ways to reduce excess model calls, choose the right sized model, use caching and increase response times whilst maintaining key quality requirements.

Ask candidates to speak about how they implemented it, how you were overseeing it, and what actions you took once it performed poorly.

Ask candidates how they would debug a poorly performing AI component which was costly due to an increase in traffic.

6. AI Evaluation, Security, and Responsible AI Practices

An application built with AI doesn’t mean it will be reliable and safe. You’ll need to know how it works in normal situations, how it reacts to unexpected inputs, and what could cause issues.

The testing of the AI along with a clear knowledge of security and responsible development will allow you to ensure the safety of both your users and your business. Such things are critical when looking for an AI engineer to develop sensitive applications.

How to Create Useful AI Testing Mechanisms

A good AI engineer will be able to identify what success looks like.

Success for the customer service assistant is when they give appropriate answers, correct information, properly escalate problems, respond within a short period of time, and have a low cost per contact. The success criteria for the classification model will be slightly different from that.

Look for someone who can create test scenarios which are representative, compare results against a baseline and perform a combination of automated tests and manual testing where required.

Generative AI models will need to be treated carefully as outputs can be inconsistent. Just having a few showy examples won’t be enough to show consistent performance.

Identifying AI Security Risks

AI applications can come with security risks that are not typically associated with standard software.

These could include prompt injection, leaking sensitive information, malicious input, insecure tool integration and incorrect permissions.

The AI assistant should not be granted unlimited access to all the databases within an organization’s internal systems and should not be capable of carrying out any actions without limitations.

Search for engineers capable of implementing different levels of protection, validating input data, storing credentials in a secure manner, implementing least privilege approach, and manual approval of sensitive actions.

Responsible AI Development Practice

The concept of AI ethics involves understanding what consequences the system’s results have and being capable of taking some action on this.

This includes such things as bias analysis, personal data security, describing limitations of the algorithm, proper disclosure, and keeping a human in the loop.

The NIST AI Risk Management Framework and the Generative AI Profile thereof can help with this.

You don’t need your engineer to be a lawyer; nevertheless, you do need your engineer to know when it is time for the security people, the privacy people, the compliance people, or the domain experts to get involved.

Method of assessing skill: Create a scenario with an AI assistant that sometimes lies or tries to access classified information.

7. Problem-Solving, Critical Thinking, and Communication

Technical skills are significant, however, AI projects need to be based on sound judgment, cooperation, and problem-solving skills as well.

A usual AI engineer is expected to work together with product managers, software engineers, executives, designers, and security professionals. Communication skills allow ensuring that the outcome will be to solve the problem of the company. Such skills are particularly useful when looking for an AI engineer for the job.

Business Problem to AI Solution

Good applicants do not start thinking about models or frameworks from the beginning; they start thinking about problems.

Let’s say your company needs to decrease the amount of time customer support staff spends answering repeatable questions. The engineer must analyze the types of questions that are asked by the clients, the quality of documentation that exists, possible negative consequences of incorrect answers, and what the business needs to achieve.

Only after all this analysis is done should they consider whether it would be right to develop a chatbot, searching engine, or some other solutions.

Critical Thinking and Structured Problem-Solving

The reason for an inaccurate response could be due to any of the following - inaccurate source data, ineffective retrieval, unsuitable modeling, integration issues, or inaccurate instruction.

A good engineer is one who systematically analyzes all such possible causes, checks all assumptions, and based on the evidence, figures out the actual cause.

Select those candidates who can answer the following questions - what would they investigate first, what evidence would they gather, and how would they check whether a certain change helped in improving the outcome?

Communication and Collaboration

There is a need for the engineer developing the AI system to explain his technical decisions using a language that everybody can understand.

The explanation should include the issues of risk and trade-off, documentation of the decisions, as well as collaboration with the other teams.

Adaptability and Continuous Learning

AI technology changes rapidly. Good candidates continue learning, but do not immediately jump to every new framework or technique.

They consider new tools with regard to their practical applicability and continue being ready to reconsider their opinion in case of more evidence.

Testing the candidate’s ability: provide a complex business problem to the candidate and ask him/her to formulate a solution, find possible risks and explain his/her solution to a non-technical stakeholder.

Interview question: A stakeholder asks for an AI-powered chatbot, but there is a simpler way to fulfill the stakeholder’s needs. What would be your approach to that discussion?

How to Evaluate AI Engineer Candidates During the Hiring Process

Once you have identified the seven most significant skills, see to it that you turn these into an evaluation system that is consistent throughout.In this way, the most suitable candidate for the job will easily be selected. Having an organized process of evaluation also ensures that the hiring of an AI engineer becomes more systematic.

Examine Previous Projects and Experience

Inquire about the contribution of the candidates to previous projects. What was the problem they solved? Which technical decisions have they made? How have they evaluated their results? What happens in case of failure?

Focus more on their practical abilities instead of depending on job titles, certifications, and technologies they used.

Conduct Practical Technical Assessments

Prepare a brief task for candidates that will relate directly to the position. You can ask a candidate to fix AI integration, increase the quality of retrieval or create an evaluation strategy for LLM applications.

Don’t make assessments extremely hard in comparison with the position and avoid puzzles of algorithms.

Build a Skills Scorecard

You can score each candidate against the following criteria:

The use of a skills scorecard will make the evaluation process more objective in assessing which skills are essential for an AI engineer recruitment process.

Common Mistakes to Avoid When Hiring an AI Engineer

In spite of having a clear hiring methodology, there are still some typical mistakes that can make a bad hiring decision.

Hiring on buzzwords: An AI framework-laden résumé doesn't say much about the ability of a candidate to build reliable applications.

Overvaluing certification: Degrees and certificates may be helpful, but should be considered in connection with practical skills.

Without production experience: The existence of proof of concept does not necessarily imply security and scalability of your application.

Hiring the perfect candidate who knows everything: Your list of requirements must be based on the nature of your project and competencies of your team.

Hire an AI Engineer Who Can Deliver Real Business Value

In assessing the AI engineer as a candidate for employment, forget about the jargon and begin to look at the seven critical domains of skill which include coding, machine learning, generative AI, data engineering, deployment, responsible AI, and problem-solving.

No matter how this combination may look depending on the specifics of your project, the key thing here is to make sure your candidate’s solution is practical, sensible, and dependable. In all, when it comes to hiring an AI engineer, this task should be solved through identifying someone who will use technical knowledge for tangible business benefit.

Looking for AI engineering experts for your future project? Gigmint.AI might become your next choice for finding AI talents and hiring solutions. First of all, define the technical requirements of your project, identify the most important skills for it and evaluate candidates by a number of criteria.