Machine learning engineers usually represent the core of AI and ML projects. With expertise in data science, applied research, and heavy-duty coding, they run the operations of a project and manage the infrastructure and data pipelines to bring the code to production. If you’re looking to embark on an AI project, you will undoubtedly need qualified ML experts on board.

However, the most challenging part is to recruit the team of machine learning engineers. The demand for these experts has grown 9.8 times over the last years. What’s more, many of them are gainfully employed by global tech corporations. So where to look for nearshore machine learning engineers? And what to take into account before choosing ML experts for your project?


Source: KDnuggets

Top destinations for outsourcing Machine Learning services


Ukraine remains one of the leading IT outsourcing destinations in Eastern Europe, offering a diverse talent pool and an attractive price/quality ratio. The country was named the “Offshoring Destination of the Year” by the Global Sourcing Association (GSA) in 2017. Moreover, 18 Ukrainian IT outsourcing companies have won a place in Global Outsourcing 100® by IAOP (International Association of Outsourcing Professionals) in 2018. So what about the Ukrainian machine learning expertise?

Having observed the Linkedin space, we’ve come across more than 2,980 machine learning specialists in the region. In addition, machine learning is gaining traction among local companies, with around 62 employers currently looking for talented machine learning engineers for their outsourced projects. Among the leading Ukrainian companies offering ML services are N-iX, SoftServe, ChatBots.Studio, eTeam, and others.

The majority of Ukrainian specialists work for local IT outsourcing companies and cooperate with businesses from the US, Canada, Western Europe, Australia, etc. Whereas the majority of European developers specializing in AI and machine learning are employed in regional subsidiaries of global corporations, which makes them difficult to recruit.

Furthermore, local IT clusters and machine learning communities organize various events for AI and ML professionals. Such events as AI Ukraine, AI and Big Data day, AI Rush, and others allow international experts to share their experience, discuss the latest advancements in the field of machine learning, data mining, text mining, Big Data, Robotics, Computer Vision, and much more.


Poland also features among top IT outsourcing destinations in Eastern Europe. According to Linkedin, its machine learning talent pool enumerates more than 5,500 specialists. As for the local companies offering machine learning services, the most prominent ones are 10Clouds, Tooploox, RIOT, MicroscopeIT, and Alphamoon. Most of these companies also have expertise in data science, big data, IoT and related areas.

According to Glassdoor, there are around 198 companies in search for professionals in machine learning and adjacent fields at the moment. Among them are global giants such as Amazon, Microsoft, Cisco, Google, Intel, Nokia, ABB, McKinsey & Company with regional offices in Poland. In fact, the majority of Polish ML experts are working for international tech corporations, which makes the talent sourcing process more challenging.

To promote machine learning expertise within the country, local communities such as Machine Learning Student Research Group organize regular meetups and conferences (e.g., “PL in ML: Polish view on Machine Learning“) to gather ML enthusiasts, entrepreneurs, and developers in one place.


Machine learning engineers in Romania are in high demand. Currently, more than 131 companies are looking for ML experts, including global corporations such as Oracle, SAP, Nokia, Amazon, Siemens, Ericsson, Microsoft, and IBM. According to Linkedin, around 2,830 machine learning professionals are already employed in local companies such as Digitalya OPS, dotNear, etc.

The Romanian vibrant tech community organizes various events and tech talks on machine learning, AI, Big Data, cloud computing, Internet of Things, and much more. Some of the most recognized ones are Bucharest Technology Week, DevTalks Romania, and Innovation Summit.


Belarus is falling behind the above-mentioned European countries in terms of diversity and availability of tech talents. Nevertheless, this IT outsourcing destination is eagerly adopting new digital trends and increasing its ML talent base. According to Linkedin, around 895 machine learning experts are employed in the country. Among local companies offering expertise in machine learning and AI, the most distinct ones are Softarex Technologies, InData Labs, Healthy Networks, DATASPECTRUM, Altoros, and Epam.

Moreover, a number of international companies that have their R&D offices in Minsk hire local machine learning experts. They include such companies as Profitero, IHS Markit, WorkFusion, Yandex, Teqniksoft, and Viber. All of them are residents of the HTP (High-tech Park), which organizes regular tech events such as AI Day Minsk, Big Data Week, etc.


Essential skills for Machine Learning engineers

Computer Science Fundamentals and Programming

The basic computer science skills required for Machine Learning Engineers include:

  • data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.);
  • algorithms (searching, sorting, optimization, dynamic programming, etc.);
  • computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.);
  • computability and complexity (NP-complete problems, P vs. NP, big-O notation, approximate algorithms, etc.);

Machine Learning Algorithms and Libraries

Basic implementations of ML algorithms are widely available through different libraries, packages, and APIs (e.g., scikit-learn, Theano, Spark MLlib, H2O, TensorFlow). However, to apply them effectively, a machine learning engineer should choose a suitable model (decision tree, nearest neighbor, neural net, support vector machine, etc.) and an appropriate learning procedure to fit the data. Moreover, one should be aware of all the advantages and drawbacks of every approach to understand how it may potentially affect other processes.

Probability and Statistics

Probability (conditional probability, likelihood, independence, etc.) and various techniques derived from it (e.g., Bayes Nets, Hidden Markov Models, Markov Decision Processes, etc.) lie at the core of many machine learning algorithms.

Moreover, many machine learning algorithms are mainly extensions of statistical modeling procedures. Thus probability and statistics provide various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data.

Data Modeling and Evaluation

On the whole, data modeling refers to the estimation of the underlying structure of a given dataset to find useful patterns like correlations, clusters, eigenvectors, etc. Data modeling is also widely used for predicting properties of previously unseen instances such as anomaly detection, regression, classification, and much more.

A professional machine learning engineer must be able to choose an appropriate accuracy/error measure and evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.). A clear understanding of measures when applying algorithms is essential for professional ML experts. Thus continual evaluation of the given model is a vital part of the data modeling process in machine learning.

Software Engineering and System Design

An integral part Machine Learning engineer’s job is software. Oftentimes, ML engineers work on a small component which later fits into a broader ecosystem of products or services. Thus a clear understanding of how these pieces work together is crucial for building appropriate interfaces for a component that others will depend on.

Python and R are among the must-have skills for the development of machine learning models. R is mostly preferred by traditional statisticians, while the majority of machine learning engineers prefer Python due to its vast availability of libraries and open source tools.

Additionally, system design is essential for avoiding potential bottlenecks and allowing the algorithms to scale well with increasing volumes of data. What’s more, various software engineering practices such as requirements analysis, system design, modularity, version control, testing, and documentation are crucial for productivity and maintainability.


All in all, an ever-growing number of companies are looking for ways to efficiently introduce machine learning into their digital strategies. It helps them have a deeper understanding of their customers, automate their processes, increase profits, and boost their overall efficiency. Thus the question of finding and choosing qualified machine learning engineers could not be more relevant.