Hire deeply-vetted AI engineers

Sometimes, it can be challenging to find an AI engineer for your company. Although it may be the most in-demand profession on the market right now, finding a skilled developer is not as straightforward as it may appear.

Hiring a developer on your own necessitates some level of software development knowledge. So if you're a non-technical manager interested in learning more about how an Artificial Intelligence developer is hired, we've put up a great resource for you.

To understand distinct AI models such as Hidden Markov Models, Naive Bayes, Gaussian Mixture Models, and others, your potential AI developers must have a thorough understanding of probability and statistics. These theories help them in their understanding of sophisticated algorithms. On the other hand, statistics is a must for programmers to be effective as AI specialists. They should know how to navigate the fundamentals of statistical methods and how to use them as a model evaluation metric. Because probability and statistics lie at the heart of AI, it's impossible to escape them.

Your potential AI developers must know programming languages such as Python, C++, R, Java, etc. Python makes it simple for them to build complex algorithms. C++ speeds up the entire coding process. Basic expertise in R is also required for them to be efficient in stats and plots. In addition, AI professionals use Java to implement mappers and reducers, so the basics of Java is also a skill not to be missed out on.

Most AI jobs require developers to deal with large data sets that they can not process using a single machine. Therefore, it has to be equally distributed across an entire cluster; this makes it essential for the developers to be efficient in distributed computing. While hiring for the same, look for experience in data management as well. Developers deal with large volumes of data, and any mismanagement can cause real big troubles.

The UNIX operating system is a multiuser computer operating system. Internet servers, workstations, and mainframe computers all use UNIX. UNIX's core characteristics—portability (the ability to run on a variety of computers), multitasking and multiuser capabilities, and its large software library—make it relevant and useful. Most of the processing in AI happens on Linux-based machines, so the developers you are going to hire must know different Unix tools such as awk, grep, sort, find, cut, tr, etc. They should be well-versed with their functions and ways to utilize them well.

You definitely wouldn't wanna hire someone who lacks knowledge in signal processing techniques because it can cost you big. The AI developer you're searching for should be knowledgeable with complex signal processing methods like bandlets, wavelets, curvelets, shearlets, and contourlets, among others. They should also have a basic understanding of time-frequency analysis and be able to apply it to their situations. Also, they should be well-versed with concepts like Convolution and Fourier Analysis.

The first step in machine learning development is pre-processing and storing raw data generated by your systems. Consider an online store that sells a wide range of goods to people all around the world. This online store will generate a large amount of data on certain occurrences. When a customer clicks on a product description or purchases a product, new data is generated, and you'll need to create Extract, Transform and Load (ETL) pipelines that process, clean up and store data, so it's easily accessible for other processes, such as analytics and predictions. So look for AI developers who know tools like AWS S3, AWS Redshift, Power BI, Tableau, Open CV, etc.

If you want to hire a skilled professional for AI development, you need to find someone who is proficient in machine learning algorithms. But that's not enough; they also need to know when to apply them. For example, if they have a dataset with a series of inputs with corresponding outputs and they want to find a model that describes the relationship, they would use supervised learning algorithms that can be further grouped into regression (when the output variable is a real value, such as "weight" or "age") and classification algorithms (when the output variable is a category, such as "yes/no").