How to Be Artificial Intelligence Quality Assurance Engineer - Job Description, Skills, and Interview Questions

Jun 22, 2021 / 5 Minutes Read / By Albert

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The increasing reliance on Artificial Intelligence (AI) in various industries across the globe has had a profound effect on the demand for Quality Assurance (QA) Engineers. With AI being used to automate processes and deliver more accurate results, the need for QA Engineers to verify code and ensure the accuracy of these systems has become increasingly important. Companies are now recognizing the importance of having a dedicated AI Quality Assurance Engineer to oversee the quality and integrity of their AI systems.

This position requires a unique set of skills that go beyond traditional QA, such as expertise in machine learning, data science, and software engineering. As AI becomes more prevalent in the workplace, the demand for AI Quality Assurance Engineers will only continue to rise, leading to increased job opportunities and career growth in this field.

Steps How to Become

Earn a Bachelor's Degree. To become an Artificial Intelligence Quality Assurance Engineer, you must first earn a bachelor's degree in computer science, software engineering, or related field.
Gain Experience. While in school, try to gain experience in the field of AI and quality assurance engineering. This could include internships, independent studies, and research projects.
Pursue Certification. Pursuing a certification can help you stand out from the competition when applying for jobs. The American Society for Quality offers certification in AI Quality Assurance Engineering.
Apply for Open Positions. After you have gained the necessary experience and earned your certification, you can begin applying for open positions. Check job listings for AI Quality Assurance Engineer positions.
Build Your Skills. Once you are hired, continue to build your skills by taking courses and attending conferences relevant to AI and quality assurance engineering. This will help you stay up-to-date on the latest developments in the field.

The role of an Artificial Intelligence Quality Assurance Engineer (AI QAE) is to ensure the quality of AI-driven products and services. To be successful in this role, one must be highly knowledgeable in the field of AI and have a keen eye for detail. A successful AI QAE must also have strong problem-solving skills and the ability to think both logically and creatively.

Furthermore, they must be able to effectively collaborate with other team members, prioritize tasks, and provide feedback and recommendations on ways to improve the product or service. Finally, they must have exceptional communication skills to ensure that their ideas are heard and understood by the team. Effectively carrying out these duties will result in high-quality AI products and services that are reliable and meet customer expectations.

You may want to check Artificial Intelligence Scientist, Artificial Learning Operations Manager, and Artificial Intelligence UX/UI Designer for alternative.

Job Description

Create and maintain test plans, test cases, and other testing documents related to AI projects.
Design and execute automated tests to assess the quality and reliability of Artificial Intelligence solutions.
Identify, analyze, and document defects, errors, and inconsistencies in AI solutions.
Participate in code reviews to ensure quality standards are met.
Collaborate with development teams to ensure that all aspects of AI solutions are tested and meet acceptance criteria.
Apply debugging techniques to diagnose problems and develop solutions for AI system failures.
Develop performance metrics and benchmarking procedures for AI systems.
Research and recommend new AI testing tools and strategies.
Monitor the performance of AI solutions in production environments.
Develop and maintain AI test automation frameworks.

Skills and Competencies to Have

Knowledge of AI and its related technologies
Understanding of software development lifecycle and quality assurance processes
Familiarity with various AI software development tools
Proficient in coding languages such as Python and/or Java
Experience with automated testing tools and techniques
Ability to develop and maintain AI-related test plans and cases
Ability to identify and troubleshoot software defects
Knowledge of AI security and data privacy standards
Ability to create detailed reports on test results
Excellent communication and problem-solving skills

Artificial Intelligence Quality Assurance Engineers are essential for ensuring the success of any AI-based system. They must possess a wide range of technical abilities, such as a deep understanding of software engineering, coding and debugging, and the ability to create test plans and test cases. They must be able to analyze the data and determine the quality of the system.

Furthermore, they must also have excellent communication skills and be able to collaborate with other members of the development team. All these skills are necessary to ensure that any AI system is working properly and meets the requirements of its users. Without these skills, an AI system may not perform as expected and could lead to serious problems down the line.

Artificial Learning System Administrator, Artificial Intelligence DevOps Engineer, and Artificial Learning Trainer are related jobs you may like.

Frequent Interview Questions

What experience do you have in AI quality assurance engineering?
How do you ensure the accuracy of AI systems?
How do you approach debugging and troubleshooting AI systems?
What methods do you use to create test cases for AI systems?
How do you determine when a test is successful?
How do you ensure the security of AI systems?
How do you monitor AI system performance over time?
What techniques have you used to identify potential problems in AI systems?
What strategies have you implemented to improve the reliability of AI systems?
What challenges have you faced in AI quality assurance engineering and how did you overcome them?

Common Tools in Industry

Test Automation Tools. These are tools that allow for automated testing of software applications. Examples include Selenium, Appium, and Robot Framework.
Test Management Tools. These are tools used to organize and manage testing activities. Examples include TestRail and Zephyr.
Performance Testing Tools. These are tools used to measure and evaluate the performance of a software application. Examples include JMeter and LoadRunner.
Static Analysis Tools. These are tools used to analyze source code for potential bugs or coding errors. Examples include SonarQube and Veracode.
Defect Tracking Tools. These are tools used to track defects or issues that arise during testing activities. Examples include JIRA and Bugzilla.
Regression Testing Tools. These are tools used to ensure that changes to a software application do not introduce new issues or defects. Examples include TestComplete and Sahi Pro.

Professional Organizations to Know

IEEE Computer Society
Association for the Advancement of Artificial Intelligence
International Association for Artificial Intelligence and Law
Association for Computing Machinery
International Joint Conferences on Artificial Intelligence
International Machine Learning Society
European Association for Artificial Intelligence
International Neural Network Society
British Computer Society
International Federation of Information Processing

We also have Artificial Intelligence Content Strategist, Artificial Intelligence Network Engineer, and Artificial Intelligence Infrastructure Engineer jobs reports.

Common Important Terms

Machine Learning (ML). a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Natural Language Processing (NLP). a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages.
Automated Testing. a process through which software applications are tested for correctness and performance.
Quality Assurance (QA). a collection of activities that ensure the quality of a product or service.
Regression Testing. a type of testing which focuses on verifying that changes to the code dont introduce new bugs or regress existing features.
Data Generation. the process of creating data sets used to train machine learning models.
Model Validation. the process of verifying that a machine learning model is accurate and reliable.
Automated Deployment. the process of automatically deploying machine learning models into production environments.
Model Monitoring. the process of continuously monitoring a machine learning model's performance in production.

Frequently Asked Questions

What is the role of an Artificial Intelligence Quality Assurance Engineer?

The role of an Artificial Intelligence Quality Assurance Engineer is to design, develop and implement test plans to ensure the quality and accuracy of AI-driven software applications.

What qualifications are required for an Artificial Intelligence Quality Assurance Engineer?

An Artificial Intelligence Quality Assurance Engineer typically needs a degree in computer science, engineering or a related field, in addition to experience in software testing, scripting and debugging.

What tasks is an Artificial Intelligence Quality Assurance Engineer responsible for?

An Artificial Intelligence Quality Assurance Engineer is responsible for creating test plans and scenarios, executing tests, debugging code, and writing automated test scripts. They are also responsible for ensuring that AI-driven software applications are functioning properly.

What tools does an Artificial Intelligence Quality Assurance Engineer use?

An Artificial Intelligence Quality Assurance Engineer typically uses tools such as automated test frameworks, continuous integration tools, and debuggers to ensure the quality of AI-driven software applications.

How much does an Artificial Intelligence Quality Assurance Engineer earn?

The average salary for an Artificial Intelligence Quality Assurance Engineer is $93,000 per year.

Web Resources

Artificial Intelligence Engineering | Software Engineering Institute www.sei.cmu.edu
How to Become an Artificial Intelligence Engineer [Career Guide] onlinedegrees.sandiego.edu
Artificial Intelligence | Cornell Engineering www.engineering.cornell.edu