A Beginner's Guide To Starting A Career in the AI Industry

Let’s take a look at what someone needs to know to qualify for the AI industry

A Beginner's Guide To Starting A Career in the AI Industry

Software Engineering is an ever-evolving industry. Navigating it can be challenging for a novice. Let’s take a look at what someone needs to know to qualify for the AI industry.

A good knowledge base for a successful career in the AI industry

There is no shortage of hype regarding Artificial Intelligence and machine learning. If you’ve already made the smart decision to become a programmer you may be considering this as the most promising job of the future.

Before you dive in and seek out a job, trusting you can Google your way through the initial stage - fair warning: the AI industry is competitive and demanding. It’s not exactly a place to enter right after a bootcamp.

This is not to discourage you, there is absolutely a way open for anyone who is motivated enough. However, keep in mind that if constant learning and honing new skills and technologies is imperative in programming, then it is doubly true in AI-related jobs.


Whether you’ve been writing code for a while or just got out of a bootcamp, there is no going around one fact: you will need math. Programming has become popular, in part, precisely because it is possible to pick it up in a few months and work from there. It doesn’t exactly require math skills.

That means that many people working in the industry today didn’t receive formal education for it. Those who opted for a computer science or math degree in their education are in a better starting position here, because AI and machine learning require mathematics.

Source: pixabay.com

So, for those who came from a different background, the first sound piece of advice is: get into mathematics. You don’t necessarily have to brand yourself as an expert, but you will need a solid grasp of

  • Statistics - a lot of machine learning algorithms are based on statistics, such as the naive Bayes classifiers.

  • Probability - sometimes machines are fed a certain amount of data that passes through probabilistic models so that they may make their own predictions in the future.

  • Algorithms - they are a formula that takes you from a specific input to the correct output.

  • Linear algebra - key in both notation used to describe algebraic operations as well as in the implementation of algorithms in the code itself.

Find a way to attain those skills and you will be able to consider this field seriously. Now that that’s out of the way, let’s get into the juicy stuff.


It’s not exactly an industry secret that not all programming languages are made equal - especially for particular tasks. Some stand out for their application in the field of AI and machine learning.

  1. Python
    Python isn’t lauded just for its application in this particular field. It’s a well-rounded language that enables you to engage in practically any other task. In the case of AI, it’s very popular because it was made as a data analysis tool and is particularly popular in the field of big data.

  2. R
    Unlike Python, which is a general language, R is a special-purpose language used for statistics. Statistical machine learning, a subset of AI, makes a lot of use of statistics.

  3. Java
    Java is another fairly general language but what differentiates it from Python is its write once run anywhere concept. It supports many types of algorithms and is multi-paradigm. It supports both procedural and object-oriented methods of programming.

Source: wikimedia.org


Frameworks are programming tools that provide customized components or solutions in order to speed up development. Probably the most useful way of thinking about frameworks is that they provide the support and basic guide for what is being built.

  1. TensorFlow
    TensorFlow can be used across a range of tasks, but it’s particularly good for the training and inference of deep neural networks. Another great thing is that it supports all three programming languages mentioned here: Java, Python, and R.

  2. Keras
    This library can run on top of TensorFlow and is used for deep learning, which is one type of machine learning.

  3. Pytorch
    If computer vision and natural language processing are what interests you, this one is a must.

Languages and mathematics are used to precisely describe to a machine, which is non-thinking, how to emulate complex, seemingly intelligent operations. Since this is a very complex task, Frameworks tackle it all and keep it in check.

One famous application of AI is Amazon. Its recommendation engines are responsible for one-third of total sales. They are leveraging AI to understand the context of customer search queries. What they want to know is why people are searching for specific products.

In this example, we can see how statistics, probability, natural language processing all work together to emulate what would traditionally be a very good shopping assistant.

Career options

There are several fields open to anyone who attains all the relevant skills. AI industry is applied extensively in business,content generation, research, e-commerce, but also more famously in self-driving tech and widely used phone/computer assistants.

Source: piqsels.com

Without going too much into detail about all the possible applications of AI and the intricacies of developing various systems, here is a short summary of potential jobs.

Machine Learning Engineer

This is what most people think of when they think of a job in the field of AI. It is considered to be one of the most sought-after careers jobs in this space. Those who opt for this field will need to hone their software skills and get a solid grasp on how to apply predictive models and use natural language processing while working with huge datasets.
The average salary in the USA: $122,185

Data Scientist

Sometimes clients need to extract useful information from a large amount of unstructured, noisy data. Data scientists collect the data, clean it up, analyze it, visualize it, and create predictive models using machine learning and predictive analytics.
The average salary in the USA: $120,500

AI Architect

AI architects differ from machine learning engineers in that they focus on the big picture of the project and develop and maintain its architecture while coordinating business and system integration. An AI architect must have a very strong grasp on the entire field because they must select the technologies that fit a client’s need as well as keep in mind the potential evolution of the project as those needs change.
The average salary in the USA: $151,570

Robotic Scientist

Anyone who is impressed by the progress Boston Dynamics Lab has made with their robots has robotic scientists to thank. They have significant formal education and their job is to build robots to perform various tasks. Whether the machine needs to go where humans can’t or shouldn’t or perhaps the task requires incredible precision, it’s up to the robotic scientist to implement the best solution. Those interested in this field should aim to have at least a bachelor’s degree related to computer science or engineering.
The average salary in the USA: $111,000

Business Intelligence Developer

This job entails developing, deploying, and maintaining Business Intelligence interfaces. These include query tools, data visualization, interactive dashboards, ad hoc reporting, and data modeling tools. Large business means large data and it’s this person’s job to make sense of it. In addition to a formal education in a relevant field, one should have experience in data mining and warehousing.
The average salary in the USA: $108,769

Source: wikimedia.org

Obviously, in addition to being sought after, these positions are well paid. Hone the pillars of knowledge provided here and get ready to write your proposal, because the world always needs more of these people.


The AI industry is a rapidly growing field that is projected to replace an ever-increasing percentage of jobs in an ever-shrinking window of time. However, to be at the forefront of this revolution of productivity, one needs a solid set of skills.

Some venues are open to all who are talented and hard-working while others are competitive enough to demand formal education. Either way, today there are great opportunities to be seized by ambitious programmers.