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Hire the Right talent for Artificial Intelligence Engineer

Artificial intelligence (AI) is an all-encompassing domain of computer science related to the development of smart machines strong enough to perform tasks that usually demand human intelligence. Even though AI is an interdisciplinary science with multifaceted approaches,  the latest innovations in machine learning and deep learning are introducing radical changes in the technology sector, needless to say, the global AI market is expanding at a staggering rate. However, these crucial factors are contributing to its rampant growth: Machine learning reaching its maturity, far-reaching application of cloud computing resources, and advancement of data collection, storage, and processing methods. AI is expected to take this disruptive technology up a notch in the next decade due to progressive computing capabilities, data diversity, and advances in deep neural networks. The demand for AI engineers has hit an all-time high, wooing employers to grab the top talent before the competitors secure them. In a bid to attract the best employees, more companies are opting for online ai assessments nowadays.

Artificial Intelligence Engineers (AI Engineers) are professionals involved in analysis and problem-solving with AI technology. Their primary duties include creating, evaluating, and implementing AI models alongside handling the AI infrastructure. Their work relies on the use of traditional software-based approach and Machine learning (ML) models. With the unprecedented demand of AI and ML in the IT industry,  there is a pressing need for  AI specialists.

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Why should you use Mercer | Mettl’s AI Engineer Assessment?

The online AI test helps recruiters to assess the technical skills and competencies of AI engineers. The test is useful for gauging the AI engineering skills of candidates. Generally, the AI assessment focuses on these essential topics: Natural language processing, Machine learning, and  Data mining. The test has been designed with the help of subject matter experts to evaluate the expertise of candidates on AI topics accurately. Employers will then have a detailed analysis of assessment results to make informed hiring decisions.
The Mettl Artificial Intelligence Assessment enables employers to identify the best professionals by helping them measure current skills and employment readiness of candidates. In view of the fact that artificial intelligence engineers work with algorithms and tools to keep systems running smoothly, it becomes imperative for these professionals to be well versed in areas like natural language processing, machine learning, data mining, and analysis, etc. On top of that, candidates with work experience of 2-5 years can take the test.

Test Details:

Number of sections1
Number of questions40
Test Duration60 Minutes


Section Details:

Technical SkillsAssess the candidate's conceptual knowledge of skills, for example, Big data, Pattern Recognition etc.


NOTE: If needed, the test can be made available in other languages as well. Please connect with us at for any such requirement.

Key profiles the test is useful for:

  • AI software development
  • AI/machine learning researcher
  • Machine learning analysis
  • Data mining and analysis
  • Fraud analytics
  • Natural language processing engineer

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Simple Setup in 4 Steps

Step 1: Add test

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Step 2: Share link

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Step 3: Test View

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Step 4: Insightful Report

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Frequently Asked Questions (FAQs)

Yes, it is possible. Please contact Mercer | Mettl for assistance.

Yes, it can be done. In case of further information, please write to Mercer | Mettl.

An AI engineer builds, evaluates, and utilizes AI models besides handling the AI infrastructure. He/she is a smart, agile problem solver who is comfortable in both traditional software development and machine learning implementations.

Following are the core areas that one can prepare to be able to ace the test:
 Comprehensive knowledge of Mathematics including statistics, probability, logic, calculus, and algorithms
 A thorough understanding of Engineering, Physics, and Robotics
 Knowledge of Bayesian networking/ graphical modeling , and neural nets
 Cognitive science theory
 In-depth knowledge of Computer science, coding and programming languages

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