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Roadmap to Become an AI Engineer: Skills, Jobs & Salary in 2024

Do you aspire to become an Artificial Intelligence Engineer? Here is a comprehensive guide on roadmap to becoming one. Learn about their salaries, job roles, and skills required.

By Lucia adamsPublished about a year ago 4 min read
AI Engineering | AI Engineer Certification | USAII

Artificial intelligence is increasing, and qualified artificial intelligence engineers are in high demand. By honing your software programming skills and researching the Artificial Intelligence landscape, you can put yourself on the route to becoming an AI engineer.

With knowledge in machine learning algorithms and techniques, natural language processing, computer vision, and deep learning, you can join the AI revolution and unlock the potential of this rapidly growing field.

According to PriceWaterCooper reports of global AI, by 2030, Artificial Intelligence looks set to make a significant economic impact, with contributions of up to $15.7 trillion predicted for the global economy.

Basic Skills Required To Become an AI Engineer:

AI Engineers are responsible for creating and managing complex algorithms, programs, and networks that form the basis of an Artificial Intelligence system. Therefore, they must have extensive software development, programming, data science, and data engineering knowledge to succeed. These skills are essential for anyone wanting to pursue a career in AI Engineering.

1. Programming:

To excel as an AI engineer, it is essential to have a comprehensive understanding of popular programming languages such as C++, Java, and Python. These programming languages can be used to implement and deploy AI models and build programs capable of making decisions, solving problems, and evaluating inputs.

2. Deep Learning:

AI engineers should possess knowledge and expertise in various deep learning approaches, which include neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN). To help in the building and application of such architectures, Keras and TensorFlow can be used as powerful tools.

3. Analytics:

AI engineers must be able to review and interpret large amounts of vital information. It can involve analyzing the data, making sense of it, and formulating potential solutions. These professionals need to be able to break down massive datasets into manageable segments and present their findings to relevant personnel. The discoveries must be explained in a precise and well-structured way.

4. Machine Learning:

Familiarity with ML concepts, algorithms, and frameworks like TensorFlow and PyTorch is essential. Knowing about supervised and unsupervised learning, regression, classification, and clustering is necessary for constructing reliable AI models and pursuing growth in AI & ML careers.

5. Mathematics:

Certified AI engineers create AI models with the help of algorithms that depend heavily on mathematics, like statistics, algebra, and calculus. To be able to interact with some of the most typical AI machine learning models, such as Hidden Markov, Gaussian mixture, and Naive Bayes, it is essential to gain an understanding of probability.

Roles and Responsibilities of AI Engineer:

AI engineers are vital to organizations that embrace AI technology. They create the AI strategy and define the issues AI needs to solve. They are responsible for constructing the development and production infrastructure for AI and implementing the strategy they devise by machine learning algorithms. Additionally, there are some more specific roles and duties AI engineers carry out. Here are some of them:

• Designing and developing AI models from the ground up and providing product managers and other key stakeholders with insights, guidance, and implementation support.

• Designing and implementing the necessary systems and processes for creating and managing Artificial Intelligence development and production.

• Creating an interface allowing machine learning models to be used as Application Programming Interfaces (APIs) to be integrated into other applications.

• Implementing automated AI systems to streamline workflow and simplify processes for the data science team.

• Working together as a collective to ensure a successful and beneficial integration of Artificial Intelligence into teams and operations while incorporating the best strategies to make the most of its use.

Salary Insights of AI Engineers:

AI engineering is a field that is gaining in popularity due to its lucrative and rapidly growing job opportunities. Glassdoor reports that AI engineers have the highest median salary for the Computer and Information Research Scientists category, at $101,991 a year in the US.

The US Bureau of Labor Statistics has identified AI engineering as an occupation with high projected job growth, expecting a 22 percent increase in positions between 2020 and 2030, significantly higher than most other occupations (8 percent).

Those in this field will likely find positions working for prominent tech firms such as Google, IBM, or Meta, helping develop and improve products, software, operations, and delivery. AI engineers qualified through advanced AI engineer certification may also find employment in government or research centres working to upgrade public services.


Artificial intelligence engineering is becoming more and more prevalent as the boundaries in this field continue to be pushed forward. Outpacing many other disciplines, AI has made immense progress in its reliability and usability, inspiring confidence in organizations, governments, and companies worldwide to take advantage of AI-based applications.

AI professionals can expect ample job opportunities with the requisite guidance and training. Knowledge about the most current requirements for an AI role is essential for any job hunter. AI has the potential to let professionals tap into market intelligence, customer intelligence, process optimization, and data analysis to make informed decisions and maximize efficiency.


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    Lucia adamsWritten by Lucia adams

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