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Dong is an MSCS graduate with 4+ years of experience in writing robust, maintainable, craftsman-quality code and transforming software engineering principles into customer delightfulness.
Naveen is a senior software developer with 8+ years of experience. He is focused on site reliability engineering with a track record of shipping products on-time and under budget.
Amine has more than 3 years of experience in the fields of quantitative analysis and information technology. He is skilled in technologies like Python, MATLAB, Regression, etc.
Aakash has 3+ years of experience in software development. He is looking to explore available opportunities within AI/ML space.
Vivek is a data science enthusiast with 3+ years of experience. He has a strong background in mathematics, statistics, and computer science.
Worried about the hassles of hiring an ML engineer? Check this guide to know about skills to look for, interview questions, and more!Read article
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Hiring an ML Engineer for your business can be a cumbersome task. It may be the most in-demand skill in the market at present, but finding a good developer is not as easy as it may seem.
Don’t worry! We're here to assist all employers who choose to recruit ML Engineers on their own. Hiring a developer on your own requires a fair amount of software development experience in general. However, if you're a non-technical manager interested in learning more about the hiring process of an ML Engineer, we've put up an excellent resource for you.
At a high level, ML Engineers should have the following skills in his/her arsenal:
Math is a vital skill for a machine learning engineer. It is also one of the fundamental taught subjects in elementary school; which is why it is the first skill on our list. But, if you're asking why an ML engineer has to master complex math at all, you're not alone. Math has a wide range of applications in machine learning. They employ a variety of mathematical formulas to choose the best ML method for a given set of data, and they can use arithmetic to define parameters and estimate confidence levels. Many ML algorithms are applications developed from statistical modeling processes, and they are extremely simple to comprehend if one has a strong mathematical background. Some of the important topics of maths that you need to test in your ML engineer include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc.
Another key aspect of a successful machine learning engineer is the ability to understand basic science and coding. They must understand data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, and other CS concepts. The good news is that they are likely to be aware of all of this if they have completed a bachelor's degree in computer science or a similar certification program. Furthermore, they should be proficient in a variety of programming languages, including Python and R for machine learning and statistics, Spark and Hadoop for distributed computing, SQL for database administration, and Apache Kafka for data pre-processing, among others. Python is a very popular programming language especially for Machine Learning and Data Science so it’s great if they are well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.
What is a crucial skill for a machine learning engineer to have? Obviously, knowing all of the common machine learning techniques. It is critical for your ML engineer to know where to use which algorithms. Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms are the three most prevalent forms of ML algorithms. Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, and others are some of the more common ones. So it’s good if they have a sound knowledge of all these algorithms before beginning their journey in your ML engineering projects.
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They should be able to model and evaluate data like a machine learning engineer. After all, data is the lifeblood of any machine learning effort. Understanding the data's fundamental structure and then looking for patterns that aren't visible to the naked eye is what data modeling entails. You must also determine whether your ML engineer is capable of managing the data. Regression, classification, clustering, dimension reduction, and other machine learning methods, for example, are dependent on the data. Naive Bayes is a classification technique that is well suited to vast data and speed, and a random forest is a regression algorithm that is well suited to accuracy. Similarly, a clustering algorithm for categorical variables is K mode while for probability is k means. An ML engineer needs to know all these details about various algorithms to contribute to data modeling and evaluation effectively.
Nobody can deny the significance of neural networks in Machine Learning. The neurons of the human brain are used to model these Neural Networks. Feedforward Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, Modular Neural Networks, and Radial Basis Function Neural Networks are just a few examples of neural networks. While it’s not necessary that your ML engineer understands all these neural networks in detail, they must know the core fundamentals.
Natural Language Processing is, without a doubt, crucial and integral to Machine Learning. In essence, NLP tries to teach computers human language in all of its intricacies. This is so that machines can grasp and interpret human language and, as a result, better understand human communication. Natural Language Processing is built on the foundation of many diverse libraries. These libraries contain a number of functions that can be used to help computers understand natural language by breaking the text down into its grammar, extracting key phrases, and deleting unnecessary words, among other things. The ML engineer should be familiar with some or even one of these libraries like the Natural Language Toolkit, which is the most popular platform for creating applications relating to NLP.
Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying an ML Engineer who will fit into your company's culture.
We will help you select the best talents and spot an ML engineer who will fit in your company culturally.
We verify if the candidate really wants to work at your company and is able to spend 5+ hours to prove it by rigorous tests. It helps us to see a developer's caliber.
Developers are asked ML related questions and made to solve tricky problems. We use open questions. The goal is not only to test developers’ knowledge – we also want to find out their way of thinking.
We provide explicit feedback on both the test task and the technical test after we have checked the developer's expertise.
You can interview the shortlisted developers to check if the candidate matches your requirements and is a good fit for your company.
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Whether you're an IT recruiter or a project manager, you know that finding top developers is critical to the success of your project. Here are some sample interview questions to use when looking for a new ML Engineer to work on your online applications.
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Here are some more ML engineer interview questions that you can ask to assess a developer’s caliber.