How to Hire Deep Learning Engineer

Whether your project focused on ML, computer vision, image or natural language processing deep learning widen the possibilities for the idea implementation.

5 min read, Aug 6

Deep or hierarchical learning comprises the multiplicity of machine learning methods concerning artificial neural networks iANN) the concept of which was developed from the investigation of information processing within biological systems.

They help to perform deeper research and detecting data that couldn’t be retrieved before due to the lack of access to higher-layer features of the initial content. Deep learning development contributed to such revolutionizing technologies as text and face recognition, NLP and robotics.

Deep neural networks (DNN) is a multilayered ANN with a linear or non-linear relationship between input and output that allow set rare data dependencies in training models. Their subspecies recurrent (RNN) enabling temporal dynamic behavior and convolutional (CNN) with regularized multilayer perceptron found application in language and acoustic modeling respectively.

Deep learning changed the approaches to the digital transformation of fields like logistics, retail and so on allowing moving forward from partial process automation to complete outsourcing of task performance to digital and robotic solutions. It also improved the efficiency of procedures concerning monitoring, analysis, and forecasting.

Deep learning applications: where and why

Considering the abilities of custom and pre-trained DNNs and their types including improvements of approaches of various AI subsets they found application in the following fields along with ML, CV, and NLP.


The most trended field focus is the personalization of the experience for every customer. Deep learning algorithms allow improving the accuracy and relevancy of suggestions generated by recommendation engines that analyze previous queries and preferences to predict needs.


These machine learning methods allow implementing more comprehensive diagnostic and other digital solutions for medical needs. In combination with computer vision approaches, they resolve issues that couldn’t be solved manually or concerns the precision of disease detection.


Neural network engineers can implement more accurate solutions focused on prevention. Bringing in face recognition and other CV and image processing projects and IoT devices together raise the level of efficiency of monitoring and visual content analysis for hazard/fraud identification.


Truck driver shortage stimulates the further development of self-driving vehicles that better fit in the modern concepts of supply chain optimization. Partly removing human factors from the business aspect it becomes more predictive and can be more accurately considered in strategic planning.


From helpful chatbots to virtual assistants DL gives the required depth of possibilities to these AI solutions changing the customer support on the core level by removing the human intermediaries. Along with novelty marketing approaches, it outlines new paths to brand engagement.

Deep learning methods are acknowledged as more efficient ways to reach ultimate AI goals. Giving that artificial intelligence is one of the props of the fourth industrial revolution it will find wider application across the business sector to speed up data-driven optimization changes.

Hire deep learning experts: who are they and when do you need them

The approach of this ML subset allows giving machines the ability of intelligent decision-making. If the project requires this level of sophistication and has an established data infrastructure artificial neural network specialists should make to the list of HR labeled to look for.

Despite that deep learning can be considered as a separate qualification it not often makes to titles of job posts and resume working experience sections. It’s usually specified as basic requirements for machine learning engineers, data scientists or computer vision and imege processing specialists.

Nevertheless, it should be namely a deep learning expert with a comprehensive knowledge basis of this family of ML methods with various types of supervising. Applied experience of building neural networks and their application for defined purposes should be present.

Other basic requirements mostly match other AI specialties:

Languages: Python, C/C++. C#, Javascript, Java, Go, etc.

Frameworks: TensorFlow, Keras, PyTorch, Caffe, Deeplearning4j, Theano, etc.

Degree: MS/Ph.D. Computer Science, Computer Engineering, Machine learning or similar

Algorithms: classification, regressions. reinforcement learning, etc.

Experience with: traditional ML, computer vision, NLP, GPU computing, etc.

Considering the specifics of the deep learning employment in various fields an applicant should have an understanding of their structure and processes. Strong analytical, learning and organizational skills and an ability to work in a team are self-explanatory.

Deep learning interview questions should help elicitation of a candidate’s qualification on both theoretical and practical sides as well as prove the relevancy and value of the available experience. An applicant should also be able to show the required level of decision-making for the position.

Deep learning developers: where to find the best ones?

Artificial neural network experts, as well as other machine learning specialists, are on high demand now. That’s why focusing just on local talents distinctly limiting professionals who are experienced enough to make to a deep learning engineer interview.

To widen the selection is better to take into consideration remote employment. In addition to freelancers, who are usually hard to manage you should also consider ML experts with expertise in deep learning companies specialized in outsourcing and outstaffing work with on a contract basis with.

East European IT market proved its trustworthiness and proficiency in working in this collaborative model with international enterprises. Lower rates in comparison to WE and the US are affecting neither qualification nor performance of the specialists.

Machine and specifically deep learning developers in Ukraine contribute to the progress and industry integration of AI working on different-stage projects worldwide. Springs as an ML solutions provider can assure experience, skills, and reliability of English-speaking members of its team and their responsiveness and flexibility towards client requirements.

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