How To Build Skills To Succeed As An AI/ML Engineer?

With new tech products hitting the market and global headlines daily, it’s no surprise that more people have been developing an affinity towards understanding how these technologies work. AI is considered one of the century’s most advanced inventions. The concept of giving machines the ability to carry out tasks just as efficiently as humans has been fancied for a long time, and AI is helping to turn that idea into reality.

AI has gained immense popularity owing to the fascination it brings to the people who are introduced to it. Many developers have been considering switching to AI for the challenge and reward it brings along. The rise in the number of AI courses in the market can be traced as obvious evidence. AI is an intellectually challenging domain and offers great monetary returns to the people who devote their time to building AI products.

But, can a developer gather all the essential skills to become a good AI/ML engineer with some coding courses alone? Let’s find out.

Building skills to succeed as an AI/ML Engineer requires combining technical and soft skills, continuous learning, and staying up-to-date with the latest industry advancements. This article outlines the basic skills necessary for any AI/ML engineer to succeed in their career and how a beginner can learn them from scratch.

Skills required to become and AI/ML Engineer

The necessary skills to become  AI/ML engineers can be classified into:

Technical skills:

1. Mathematics: Every computer science field demands high-level logical reasoning to tackle the problems presented. The issues that engineers are presented with are often mathematical. Therefore, an AI/ML engineer must know statistics, calculus, and algebra to work with AI/ML algorithms. A beginner can get started with mathematics by seeking guidance from books or math courses with free certificate.

2. Programming: Python is the most popular language for developing AI models. While a beginner would be fine with coding courses to get an idea of what programming is, an experienced programmer would likely use books, research papers, and mentorship programs to attain the skills needed to solve advanced problems.

3. Knowledge of frameworks: Deep learning is a subfield of AI that most AI/ML engineers are likely to encounter at some point in their careers. Deep learning algorithms make advanced natural language processing, facial, voice, and gesture recognition programs. To develop efficient products, it’s essential to integrate deep learning into them. Thus, a developer must become accustomed to common deep-learning frameworks like Tensorflow, PyTorch, Keras, and Theano. If you’re a developer coming across these terms for the first time, you can explore more by starting with artificial intelligence courses.

Soft skills:

1. Communication: Communication is an essential skill required for success in all fields. Artificial intelligence is no exception. As a developer, you would be required to explain your solutions to different teams who may not necessarily have a technical background. It would be best to explain things in layperson’s terms so that everyone can understand your solution easily.

2. Ethics and bias: People’s opinions on the development of artificial intelligence are multifaceted. As a developer, you must be fully aware of the social considerations of the product you’re building. It’s important to acknowledge that the AI model may showcase some bias. It’s best to do whatever’s necessary to prevent such instances.

A great way to stay updated with the latest AI debates and discussions is by attending conferences and research programs. Though, these would require you to have a coding certification.


To become a successful AI/ML engineer, one needs a combination of technical and soft skills, continuous learning, and staying current with industry advancements. Technical skills include mathematics, programming, particularly in Python, and knowledge of deep learning frameworks. Soft skills include effective communication, ethics, and bias awareness. Beginners can start with courses and books, while more experienced engineers can benefit from attending conferences and seeking mentorship.