Top 7 Highly Paid AI Jobs
On how the emergence of neural networks affects the labor market, They say everything. Because it is impossible not to talk about it. in the opinion of experts (and not only them), some professions will soon be consigned to the dustbin of history, while others will become incredibly popular. Let's figure out what awaits employers and job seekers in the near future and which specialists in the field of AI are ready to pay the most.
What's in the past
For thousands of years, humans had to work hard to feed themselves: literally grow enough food. The invention of land cultivation devices and advances in animal husbandry made people's lives easier and increased their chances of survival. However, the division of labor and technological advances put people in an unequal position: some continued to produce their daily bread, while others went to work in factories where they could earn more money.
In the 19th century, toward the end of the Industrial Revolution, factory workers became afraid of being replaced by machines and began to hinder scientific and technological progress. From the Luddites' fight against machines did not hold even the fear of the death penalty. However, it was impossible to stop the development of industry: engineers created increasingly complex and productive machines, and workers were still needed to service them – albeit increasingly qualified ones.
What now
The current situation is somewhat similar to the state of affairs at the beginning of the 19th century. Neural networks seem to be able to replace a large number of professions: from artists to programmers who are engaged in the development of artificial intelligence. But in reality, everything is not so simple.
The labor market is being restructured in the 2020s. The reason for this is global economic shifts and the emergence of new technologies. This concerns both global situationand the state of affairs in Russia.
If we talk about our country, there is a significant gap in the labor market. Large technology companies refuse from “manual” labor, automating routine operations. At the same time, in a number of companies there is still a great demand for employeesdoing work that can be automated — but managers are not prepared to invest in reform. In addition, there is a high demand for workers who perform physical operationswhich today cannot be entrusted to machines, for example, to couriers.
But let's discuss the state of the labor market in the technology sector in more detail. There is a shortage of personnel here – there is a lot of talk about this now They say. At the same time, employers are waiting for specialists at the middle level and above, and Jun needs to work hardto find your first job. Recruiters are really hunting for experienced developers – and their offers are much more interesting than a “young ambitious team”, cookies and voluntary health insurance with dentistry.
Who are they willing to pay?
The salaries of different AI specialists may vary depending on the country, company and, of course, grade. Let's see who employers in Russia are looking for, what requirements they have for candidates and how much they are willing to pay.
Anna Veklich
Co-founder of the AI aggregator of neural networks GPT4Telegrambot, media about AI “Hi, AI! | neural networks”, author of the course “Neuroliteracy”
There are specialists who develop AI: data scientists, machine learning specialists, mathematicians, scientists. There are those who know how to adapt AI to market needs and solve various problems using ready-made AI services or by retraining them for the company's tasks: LLM retraining specialists, developers in different programming languages, and others. And there is a third type of specialist: everyone else. They have learned to work with AI services as users (like we used to learn to use a PC) and have become valuable employees who can complete tasks faster, delegating a lot to neural networks. But at the same time, they themselves do not influence the work of the services in any way, they use what the authors of AI resources provide. These are marketers, salespeople, editors, journalists, sometimes methodologists, economists, lawyers.
There is a high demand for all of these specialists now. It is just in different market sectors. The first are the highest paid, work in top corporations or create their own companies. The second work in medium-sized businesses, agencies, and as heads of digitalization departments. And the third are essential specialists who should soon appear everywhere: from entry-level positions to project managers. Employees in many industries today must be able to work with AI, just as we can use the Internet, a computer, or Word documents.
It should be noted that companies often look for a person with a pool of responsibilities that are at the intersection of different professions – for example, an ML engineer with a deep understanding of the principles of computer vision or a data engineer, data analyst.
Intelligent Systems Architect (AI/ML architect)
What it does: is engaged in the design of complex AI systems and their integration into the business processes of companies.
How much does he earn: up to 550,000 ₽.
What should you be able to do:
understand the principles of operation of machine learning algorithms;
know programming languages (most often Python, Java, C++ are required);
work with frameworks and libraries;
have system design skills;
work with databases.
Data engineer
What it does: develops systems for storing and processing large volumes of data necessary for training AI models.
How much does he earn: up to 500,000 ₽.
What should you be able to do:
design, create and maintain data warehouses;
work with cloud platforms for data storage;
configure ETL processes (extract, transform and load data);
develop and optimize SQL queries;
apply data quality assurance methods (cleansing, deduplication, profiling);
use tools for working with big data: Hadoop, Spark and similar;
ensure data security.
Vladislav Shevchenko
Leading engineer for the development and implementation of machine learning models at Alfa-Bank, mentor of the online master's program “Data Engineering” at Netology
After graduating from university, I worked as an engineer at Rosneft for two years. It was there that I developed a great desire to create my own business solutions and projects. The rest of my career was pretty standard: first, consulting and business analytics, then I decided that I wanted to not only create processes and solutions, but also understand how everything works from the inside. So I started learning big data programs like Pentaho and Hadoop, and found my first job as a data engineer at one of the leading banks. By the way, I have had this desire to analyze everything down to the smallest detail since childhood: I often took apart toys.
ML engineer (machine learning engineer)
What it does: creates and trains machine learning models that underlie many AI solutions.
How much does he earn: as a rule, up to 600,000 ₽.
What should you be able to do:
design and train machine learning models;
work with various algorithms and methods of machine learning: linear regression, decision trees, neural networks, etc.;
tune hyperparameters of models to improve their accuracy and performance;
conduct experiments and analyze the results;
develop and implement systems for automatic learning and retraining of models;
ensure interpretability and explainability of models;
apply methods to combat overfitting and other machine learning problems.
Natalya Badanina
Engineer at NIIDAR
To start working with ML, you will need programming skills in Python, sometimes you can come across R and Julia in work. They are used mainly for statistical analysis and high-performance computing. In Python, you need to study some more libraries, such as TensorFlow, PyTorch, scikit-learn.
At the start of a career, practical experience will greatly help. For example, participation in pet projects, internships or hackathons. The Kaggle platform offers data analysis competitions where you can apply and improve your skills in solving practical problems.
In terms of theoretical knowledge, it will be useful to know linear algebra, statistics, and probability theory to understand machine learning algorithms.
It is also important to know the specifics of the industry in which you are going to apply AI, be it finance, medicine, marketing or industry.
NLP specialist (natural language processing specialist)
What it does: is engaged in natural language processing, which allows AI to understand and generate text.
How much does he earn: up to 700,000 ₽.
What should you be able to do:
work with text data using NLP methods and tools;
apply machine learning algorithms to natural language processing;
create models for text sentiment analysis, keyword detection, information extraction, and other tasks;
use libraries and frameworks for NLP: NLTK, spaCy, TensorFlow Text and others;
develop systems for automatic translation, speech recognition, text generation and other NLP applications.
Computer vision specialist
What it does: works on image and video recognition systems that are used in various AI applications.
How much does he earn: up to 600,000 ₽.
What should you be able to do:
work with computer vision algorithms and methods;
create and train models for object recognition, location, classification and other tasks;
use libraries and frameworks for computer vision: OpenCV, TensorFlow, PyTorch and others;
develop systems for automatic detection, tracking, analysis and interpretation of visual data.
Data analyst (data scientist)
What it does: analyzes large volumes of information, identifies patterns and trends that can be used to improve AI algorithms.
How much does he earn: up to 550,000 ₽.
What should you be able to do:
work with various data sources;
clean, transform and process data;
apply methods of statistical analysis and machine learning;
build models and make predictions based on data;
visualize the results of the analysis in the form of graphs and diagrams;
interpret the results and formulate conclusions.
AI project manager
What it does: coordinates the work of a team of specialists, is responsible for the successful implementation of projects for the development and implementation of AI solutions.
How much does he earn: up to 400,000 ₽.
What should you be able to do:
define the goals and objectives of the project;
draw up a work plan and estimate resources;
coordinate the work of a team of specialists;
control the deadlines for completing tasks;
ensure the quality of the results;
manage project risks;
present the results to the customer.
What's in the near future
It cannot be said that the situation on the labor market is changing rapidly now: specialists with experience, the ability to independently find solutions, the ability to self-study and developed flexible skills are still always in demand. Such candidates are ready to be provided with the most comfortable working conditions and well paid.
However, it is possible to identify trends that distinguish today's state of affairs from the past decade:
Routine procedures are increasingly automated – companies need fewer and fewer people to perform manual labor and simple operations (including intellectual ones). Because of this, it is more difficult for entry-level specialists to find work.
New technologies are emerging more and more oftenThe consequence of this is the need to devote more time to training.
Competition among employers is growing. In Russia, this fact is aggravated by the large number of IT specialists who have left for abroad.
Employers are looking for candidates with a good foundation. Developers with a mathematical background are in demand, as well as specialists in related professions, such as bioengineers or specialists with a deep understanding of physical processes.
Business has a separate demand for specialists who are proficient in neural networks at the user level. Of course, their income level may not be as high as that of professionals developing and implementing AI solutions, but to remain competitive in any profession, you will have to master writing prompts.
Vladimir Lee
Head of Machine Learning Department at Netology
I will refer to the recent performance NVIDIA CEO Jensen Huang, where he said: “There is no point in teaching children to program now, since programmers will soon be completely replaced by neural networks. But at the same time, there will be new profession: a Prompt engineer who knows how to put the right requests to AI, and in the future he alone can replace an entire department of programmers.”
In general, this is believable, but it is necessary to understand that, in turn, the specialty of a prompt engineer will also expand its functionality, since it is necessary to be able not only to make the correct prompt, but also to integrate the created solution into a productive environment, implement it into the relevant business processes, therefore, engineering and architectural skills are still needed here.
On the other hand, NVIDIA is a monopolist in the field of manufacturing chips that run AI. Thanks to this, the company's capitalization has grown by 1,500% over the past 4 years. Therefore, the forecasts are quite reasonable, but it would be strange if the head of the company – the leader in the development of AI – expected a different development of events.
Neural networks will be a part of our lives: arguing with this and trying to change something is useless. They will not lead to the mass disappearance of a number of professions, but they will significantly change the approach to recruiting and organizing work within companies.
The demand for specialists involved in the development and implementation of artificial intelligence will only grow. Employers are ready to offer such workers good conditions, but they also have high requirements. Because of this, experienced specialists must constantly improve their qualifications and study new technologies, and newcomers must develop a base as quickly as possible, simultaneously studying applied disciplines.
To grow, you need to step out of your comfort zone and take a step towards change. You can learn something new by starting with free classes:
Or open up prospects with professional training and retraining:
