The constantly growing technology sector offers many intriguing and creative employment options. Artificial Intelligence (AI) Engineering is a fast-growing field that can provide you new challenges and infinite opportunities for career growth.
According to the World Economic Forum’s report, Artificial Intelligence is expected to provide 97 million employment opportunities by 2025. The employment growth has already inundated the market since the demand for artificial intelligence engineers has increased in recent years. Therefore, the world needs well-qualified artificial intelligence engineers.
- The AI market is expected to reach USD 190 billion by 2025, according to Marketsandmarkets research firm.
- Gartner forecasts that this year, AI will create 2.4 million jobs.
- IDC says global AI systems expenditures will exceed USD 57.6 billion by 2021.
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In this blog, let’s discuss who an AI engineer is, how vital their role is and how to become an Artificial Intelligence Engineer.
Artificial Intelligence Engineering – What is it?
Essentially, algorithms, computer programming, neural networks, and other technology in AI applications and approaches are called artificial intelligence engineering. Typical practical uses in business, science, and other sectors of life include these technologies and applications.
Therefore, an artificial intelligence engineer must efficiently collect data from various sources, design algorithms, construct and test models, and then implement these models to produce AI-enabled apps capable of carrying out complicated tasks.
Why an AI Engineer – What do they do?
The design issues arising out of merging the logic inherent in traditional applications with the learning logic of machine learning models are unique for AI engineers.
The following considerations are:
- Work with many infrastructures, including chips (GPUs, FPGAs), on-site and cloud systems.
- Understand how the machine learning process (e.g., feature engineering, model construction, and model validation) adjusts to support ongoing pipelines for development
- Decide when a model is ready for use and evaluate its precision over time to determine when it needs recycling or replacement.
Top software engineers in an organization are ideally positioned to become AI engineers since their expertise and experience with embedding learning algorithms most probably depend on the complete stack application development.
However, the computer science freshers will also meet some needs for AI engineers through their mixture of programming knowledge, strong mathematical and statistical principles, and data science competencies, enhanced by choosing a machine’s choice.
An artificial intelligence engineer works with algorithms, neural networks, and other tools to progress on artificial intelligence. These individuals can deal with diverse artificial intelligence types in many businesses – including artificial healthcare intelligence, artificial retail intelligence, or artificial public planning intelligence.
Engineers can also pick between weak or powerful artificial intelligence projects where different configurations focus on various capabilities.
They usually have computer science degrees or educational backgrounds – many of them have their pilot projects to present potential employers. Some work in the open-source community network that develops open-source AI tools.
Roadmap to become an AI Engineer:
You can pursue various pathways to become an AI developer as the artificial intelligence sector is somewhat new. You could take these steps if you are looking to start a career as an AI engineer:
- Get your graduate degree: You will probably have to meet some educational criteria to become an artificial intelligence engineer. Employers generally ask for a bachelor’s degree in an associated discipline: computer science, data science, IT, statistics, and finance.
- Improve your skills: You need specific essential technical, soft skills and abilities to succeed as an AI engineer, including:
- Understand statistics, probability, and linear algebra – AI models are developed using algorithms, which rely significantly on statistics, algebra, and calculus. In addition, you need to be aware of the likelihood to interact with some of the most common models of artificial intelligence, such as Hidden Markov, Naive Bayes, and Gaussian mixtures.
- Programming skills – You need to know common programming languages, such as C++, Java, R, and Python, to succeed as an AI engineer. That’s why you must design and deploy your AI models using these programming languages.
- Analytical skills – AI engineers often require significant data to be reviewed and interpreted. You need to analyze information, create insights and find potential solutions to do so effectively.
- Business acumen – You can resolve the critical problems of an organization with successful artificial intelligence projects. With commercial understanding, you can transform your technical ideas into efficient business projects. That’s why you should at least have a fundamental awareness of how the company functions, its target group, and the top competing players in the company, regardless of what industry you are employing.
- Communication skills – In many cases, artificial intelligence developers are required to communicate data and technical information to various people with varying levels of technological competence.
- Collaboration skills – Persons in this industry frequently can operate effectively and efficiently in a team from other AI developers and IT specialists.
- Critical thinking capability – You have to develop prototypes to develop novel AI models and solutions quickly. In other words, you should be in a position to find several viable solutions to a single issue.
- Certification courses: Through practice or a certification course, you can get the skills mentioned above. It is highly suggested to complete a certification course in data science, machine learning, or artificial intelligence to be an AI engineer.
AI Engineering is an emerging subject to design tools, systems, and procedures that enable artificial intelligence to be applied in reality.
By 2021, the increase in AI is anticipated to accelerate the company value by almost USD 2.9 trillion and recover a worker’s productivity by 6.2 billion hours, reports Gartner. In addition, by 2021, IDC expects that 75 percent of commercial company apps are using AI.