3 Artificial Intelligence (AI) interview questions for 2021


According to a Hackerearth survey of 2,500 developer recruiters and hiring managers, the most in-demand technology skill for 2021 will be artificial intelligence / machine learning (AI / ML). This is an incredibly large field and not all jobs require the same skills. As your organization competes for talent, don’t let the enthusiasm cloud your judgment.

It’s important to ask the right questions to really assess a candidate’s suitability for the job. It’s important to note that the interview shouldn’t just delve into the inner workings of AI; rather, it should give you an understanding of whether a candidate can translate their AI skills into business results.

[ Need to speak artificial intelligence? Download our Cheat sheet: AI glossary. ]

Here are some questions we asked LatentView Analytics to help us get to the heart of the interview.

1. What is your experience with AIOps or MLOps?

While not all AI roles require the same skills, certain skills or experiences are almost universally valuable.

We’ve worked with many organizations that have invested millions of dollars in AI initiatives, and the pandemic has made their boards question how these led to real growth. The focus is now on empowering AI and how to truly operationalize AI. These are better operations, not new big projects. For this reason, AIOps has come to the forefront of desirable skills within AI.

[ What skills are hottest right now? Read also: IT careers: 10 critical skills to master in 2021. ]

In the same way that DevOps revolutionized the way we develop applications, AIOps standardizes AI pipelines, generates in-depth performance analysis, and automates workflows. This leads to greater efficiency and also helps teams iteratively improve algorithm performance using real-time metrics. Application Performance Monitoring (APM) contributes immensely to this, and we are looking for candidates who have experience working with APM platforms precisely because of how their performance-driven background drives the initiative towards goals. measurable business.

[ Read also: What is AIOps? Benefits and adoption considerations. ]

2. What are the new use cases of AI in X domain?

This question really depends on the role for which the candidate is applying. For example, if your business is in banking / financial services, you can apply for loan approval.

The candidate’s response should focus on a few use cases that show they are knowledgeable in your field. It must also demonstrate technical expertise in terms of relevant algorithms. In the case of loan approval, for example, the KNN algorithm is a good supervised learning algorithm because it efficiently sorts requests into two classes: approved and disapproved.

The candidate’s response should focus on use cases that show they are knowledgeable in your field. It must also demonstrate technical expertise in terms of relevant algorithms.

Most importantly, you want the candidate’s response to be translated from technique to practice. How can you make sure your training data is unbiased and doesn’t lead to the wrong person’s disapproval? How can advancements in Explainable AI (XAI) help us understand and justify automated loan decision-making for disputed outcomes? If they think about these implications, you know they understand your business and the potential challenges they will face on the job.

[ How does your AI strategy handle security and privacy? Read also: Artificial intelligence (AI) and privacy: 3 key security practices. ]

3. What emerging areas of AI research are you passionate about and why is this important to our business?

Since the supply of qualified AI experts is relatively limited and the field is advancing at breakneck speed, your new AI recruit may well be more at the forefront of AI research than many of their peers.

This question gives you a good idea of ​​how their role might evolve in the future and how they perceive the technological trajectory of your business. It also shows that they are following their domain and will likely continue to do so in your business. This is important because flexibility and the desire to improve one’s skills are two of the most important soft skills that a person in a high-tech field can have.

Finally, AI experts often become internal advocates for certain AI technologies or research areas in which they have personal experience or preference. You should check this during the interview, as some people will be looking to make a tangential use case applicable to your business just because they are passionate about it. Look for pragmatic candidates in their responses to ensure future AI initiatives are focused and relevant.

2021: the year of AI responsibility

Businesses need to be strategic and holistic as they move from AI to AI responsibility. This change doesn’t have to happen only at the structural or conceptual level: you don’t just want buy-in from the CIO or CTO, who then have to constantly refocus their engineers. It must depend on what AI hires you do today and tomorrow. This way you are aligned with the bottom-up business-driven AI.

[ Get the eBook: Top considerations for building a production-ready AI/ML environment. ]


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