As artificial intelligence (AI) specialist, I understand that the development and use of AI technology will have a significant impact on society for many years to come due to advances in computing power, capacity, speed, and data diversity, as well as the continued development of deep neural networks.
AI makes people’s lives more productive and smarter by integrating machine learning (ML) algorithms into various products and services. At the end of 2021, HackerEarth, which helps organizations meet their technical recruitment needs, surveyed more than 2,500 staff and engineers. It showed that 30% of respondents said they expect to hire more than 100 developers in 2022.
Many industries have realized that the combination of AI machines and human employees can boost productivity and generate huge revenues in a competitive market. Even governments have begun to allocate millions of dollars for research and development in the field of AI. This growth is fueling the demand for talent in a number of related disciplines, including AI engineering.
In a recent study, Gartner, Inc. noted that by 2025, 10% of businesses adopting AI engineering best practices will benefit at least 3 times more from their AI efforts than 90% of businesses that do not. But what is so special about this area?
AI engineering is one of the new engineering disciplines focused solely on the creation and development of intelligent tools, machines, and systems to improve the standard of living in society. AI Engineering covers a wide range of computing power and massive datasets with the integration of machine learning algorithms.
Essentially, AI engineering is the use of algorithms, computer programming, neural networks, and other technologies in the development of AI applications and methods. These methods tend to find practical applications in commerce, science, and other aspects of life.
In contrast to the prevailing rush to develop capabilities and improve individual tools, AI Engineering asks a different set of questions: how can AI help people achieve mission results? What are the limitations of AI systems in practice today? How can we ensure that ethical standards are met when deploying AI systems?
The need for an engineering discipline to guide the development and deployment of AI capabilities is imperative. For example, while an autonomous car performs well on an empty race track on a sunny day, how can it perform just as well during a hailstorm in New York City? AI Engineering aims to provide a framework and tools for the proactive design of AI systems to operate in environments characterized by a high degree of complexity, ambiguity, and dynamism. The discipline of AI engineering aims to equip practitioners to develop systems from the enterprise to the edge, anticipate requirements in changing operating environments and conditions, and enable the transformation of human needs into understandable, ethical, and therefore trustworthy AI.
AI engineers can quickly add machine learning capabilities to business-critical systems such as enterprise resource planning (ERP), customer relationship management (CRM), and mobile device management (MDM). They can also develop AI business applications from scratch.
AI engineering enables organizations to create hybrid operating environments that combine data science, data engineering, and software development. Successful AI projects will benefit the enterprise and address the associated business pain points—whether internal or related to customer cases, supply chains, or other external factors. AI engineers can decide which projects will benefit the most from weak or strong AI.
Engineers with artificial intelligence play an incredibly important role in today’s economy, especially in sectors and verticals where AI technologies have already had a significant impact. Some industries that use these technologies include:
As real-time analytics and human thinking become more important in a number of industries, and as underlying technologies continue to evolve, the demand for AI engineers is likely to increase in the coming years.
AI engineering is one of the most promising branches of the industry and now is the perfect time to develop it. This is perfectly illustrated by the annual list of new vacancies published by the platform LinkedIn, which puts the position of an artificial intelligence specialist in the first place. Hiring for this position has grown by 74% annually over the past four years.
Here is another example of the demand for AI Engineering. According to an Indeed report, the Machine Learning Engineer ranked number one on the annual Top 25 Jobs list, citing a 344 percent increase in job postings over the past few years and an annual base salary of $146,000. Roles for AI engineers are being created across sectors ranging from technology and the automotive industry to higher education and sports.
However, while this demand currently outstrips supply, this may change in the coming decade as more and more organizations launch professional development programs for existing employees, universities begin to offer more courses, and more aspiring individuals begin the self-learning process. to gain an advantage over other candidates.
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