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How To Become an AI Engineer 2023 Career Guide
Data Science

How To Become an AI Engineer: 2024 Career Guide

11 minute read | March 15, 2023
Sakshi Gupta

Written by:
Sakshi Gupta

Ready to launch your career?

The career outlook for artificial intelligence (AI) engineers is only looking rosier over time. The Bureau of Labor Statistics projects that, in the next decade, there’s going to be a 21% increase in computer and information research jobs, which includes AI engineering roles.

21% may not sound like staggering growth, but when you consider that there’s already a lack of qualified AI professionals, the picture starts to become clearer. AI engineers are in high demand, and given this, it’s not uncommon to see entry-level AI engineering jobs approaching six-figure salaries. 

Want to capitalize on this trend? Then you’re in the right place. Below, we’ll walk you through how to become an AI engineer and point you toward the resources that can make this career a reality for you. 

Ready? Then let’s get started. 

What Does an AI Engineer Do?

AI engineers are software professionals who build tools and systems using various artificial intelligence techniques. That includes approaches such as machine learning, deep learning, and neural networks. The goal is to increase the efficiency of systems and make better use of data to drive business decision-making. 

How To Become an AI Engineer: A 11-Step Guide

How To Become an AI Engineer A 10-Step Guide

Landing a job as an artificial intelligence engineer takes a combination of strong technical skills, a strong theoretical foundation, and soft skills like effective communication and creative thinking. Let’s find out how you can go about gaining the key skills required to work in the artificial intelligence field.

  1. Complete a Course

  2. Learn the Fundamentals

  3. Get Educated (And Keep Learning)

  4. Learn a Programming Language

  5. Build and Hone Your Skillset

  6. Gain Relevant Experience

  7. Create a GitHub Profile

  8. Find a Mentor

  9. Your Network Is Your Net Worth

  10. Consider an Internship

  11. Ace the AI Engineering Interview

Complete a Course

AI is a relatively new field, and one that is constantly changing. The best way to master the skills you need to launch your career is with a bootcamp. It’s the quickest and most convenient way to get the up-to-date skills you need to pursue a career as an AI-engineer.

Learn the Fundamentals

Artificial intelligence is a field that’s often quite misunderstood. Too much of the conversation surrounding it is driven by a fear of it being a mortal threat to human beings. For those who want to work in the industry, it’s important to see past this and gain a solid understanding of how artificial intelligence systems actually work. 

So what is artificial intelligence? Artificial intelligence is a broad field of study that attempts to produce computational systems that can mimic human intelligence. To make that happen, various technologies like neural networks, natural language processing, deep learning, and adversarial networks are used. 

A simple example of AI is the digital assistants we use in our everyday lives, like Siri and Cortana. These voice assistants use our voice commands and the data on our phones as the training dataset. They learn from that data and are eventually able to better detect what we want and serve up results faster. 

Get Educated (And Keep Learning)

Artificial intelligence professionals take a few different routes on their way to a job in the field. Let’s take a look at what those are. 

Four-Year Degree

The most common path to an AI engineer career is through a college degree. You can go with either a degree in computer science or mathematics. Both provide a range of courses that are relevant to AI. The rest you will have to pick up on your own, either on the job or independently. 


The less conventional but often more convenient way of becoming eligible for career opportunities as an AI engineer is by working through bootcamps. There are various providers who offer bootcamps on artificial intelligence and related fields like machine learning

There are a few reasons why bootcamps have become an attractive option. For starters, they’re a flexible mode of learning. You can choose whether you want to study online or offline, how long you want to spend on the course, and can select one after you’ve vetted the instructors. 

Bootcamps are also a lot more affordable than a college degree. Some allow you to pay after you’ve landed a job. And, some bootcamps provide job guarantees and career support, making this an easy choice for most learners. 


The Internet is full of resources for those who want to study AI on their own. If you’re somebody who is an autodidact and trusts yourself to stay motivated through the entire process, then this is the most affordable and flexible path to choose. 

Learn a Programming Language

how to become an ai engineer, Learn a Programming Language
Source: JavaTpoint

Not all AI engineers use programming, but most know how to code with at least one programming language used for AI. Here are the programming languages that you can consider starting your journey with:


Python is among the most popular programming languages used to build AI and machine learning applications, as Python has a range of libraries for AI and machine learning. If you’re just starting out, then consider starting with Python because of its intuitive syntax and simplicity. 


Java is a programming language that is used for a wide number of applications, including artificial intelligence systems. The advantage of using Java for AI is that there is a huge community around the language and you can always get help if you need it. Learning Java also means that you can move between different kinds of operating systems and device types and still be able to develop programs for them easily. 


R is a programming language that’s focused on statistical analysis. It provides various libraries that cater to those who have a knowledge of statistics and want to use it to develop artificial intelligence models. Make sure that you have the statistics and mathematics knowledge required to work with such a programming language before beginning your journey with R. 


Haskell, Lisp, C++, and Scala are also languages that are used by artificial intelligence engineers. These are usually selected when they provide a specific advantage for a certain application that’s being built. If you’re just starting out, stick to Python, and then learn other languages if a project demands it. 

Build and Hone Your Skillset

how to become an ai engineer, Build and Hone Your Skillset

Knowing how to program is only a small part of the overall skillset of an artificial intelligence engineer. You need to have a firm understanding of several other areas and skills if you are to become proficient at this job. Let’s take a look at what they are. 

Technical Skills


Algorithms are the foundation of all software development. So if you’re applying for AI roles, then recruiters will test you on your knowledge of algorithms to make sure that you understand how to write them. 

Machine Learning

Machine learning is an essential skill when it comes to working as an artificial intelligence engineer. You need to be proficient at using techniques like logistic and linear regression to build robust machine-learning models. A broad understanding of machine learning will significantly boost your ability to land AI job roles. 

Image and Language Processing

Most AI engineers work on either image- or language-based data. So it’s important that you understand image processing and how to work with natural language processing systems. There is huge demand nowadays for AI engineers who have hands-on experience in these areas. 

Soft Skills

Working as an artificial intelligence engineer isn’t all about crunching numbers and constructing models. Let’s take a look at the soft skills that you will need to succeed in your career journey. 

Analytical Thinking

Working as an AI engineer means that you’re constantly dealing with a wide range of problems and have to deal with data coming in from a variety of sources. As an AI engineer, you’ll have to analyze problems quickly so that you can choose the most effective path forward. 

Business Acumen

Most successful AI engineers out there have some business skills. They understand how business processes work and have a basic understanding of how business intelligence guides business decisions. This industry knowledge is something you should work on gaining slowly so that you can situate your work within the context of organizational goals. 


Your communication skills will be put to the test as an AI engineer. This is a job that requires you to work in tandem with software engineers, project managers, product managers, and other professionals, including nontechnical stakeholders. You’ll have to quickly learn how to listen actively and communicate so that your point gets across politely and effectively. 

Gain Relevant Experience 

Here are a few ways that you can gain some AI experience if you haven’t yet landed an entry-level role. 

Volunteer Work

There are plenty of non-profit organizations and small businesses that hire volunteer engineers. Look up local organizations in your area to see if they need a helping hand in their IT or software department. Then, you can add those projects to your portfolio.

Freelance Work

Freelance projects give budding AI engineers the opportunity to work on projects of their own choosing. Make sure that you convey your capabilities right at the outset and have a point-of-contact who can help you if there are any hold-ups in the process. 

Open-Source Projects

This is the easiest way to dip your toes in a production environment as an artificial intelligence engineer. Open source projects allow you to read codebases and see how AI engineers achieve the things they do. Once you know how to read code, you can move on to identifying bugs and recommending fixes. 


Hackathons are competitions where you will be given a small amount of time to build a specific AI application. It is a low-stakes situation where you learn a lot of new things quickly and walk away with a project that you can add to your portfolio.

Create a GitHub Profile

how to become an ai engineer, Create a GitHub Profile

An easy way to demonstrate your practical expertise is by showing your work on GitHub. The platform serves as a portfolio of projects that includes your code base. You can also use it to collaborate with other engineers and contribute to a range of other projects. 

Find a Mentor

Mentorship gives candidates the kind of leg up that few other things can as they work their way toward a career in AI. Mentors provide you with tailored support and can help you make key decisions using information gained through years of experience. 

If you’ve come through a college or bootcamp, then you can turn to professors and instructors for mentorship. You might also consider approaching industry professionals, such as senior AI engineers, to serve as mentors to you. Websites like MentorCruise and CodeMentor can be helpful towards that end. 

Your Network Is Your Net Worth

Having connections in the industry is a huge advantage for those hunting for jobs in artificial intelligence. Here are a few ways to build those connections:


LinkedIn allows you to easily search for and connect with engineers and managers in the artificial intelligence space. You can slowly build a relationship with your connections by interacting with their posts and showing genuine interest through thoughtful comments. You can then take that forward by requesting a phone call or in-person meeting with a specific ask. 

Conferences and Meet-ups

Most major cities have communities that cater specifically to AI professionals. Attending these events is a great way to quickly meet a lot of people who can help you with your technical skills or in your job hunt. 

Online Communities

The following online communities provide an environment where you can keep up with developments in the industry and meet others who are passionate about AI technology:

Get To Know Other Data Science Students

Jonathan King

Jonathan King

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Haotian Wu

Haotian Wu

Data Scientist at RepTrak

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George Mendoza

George Mendoza

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Consider an Internship

Most companies hiring AI engineers also have internship programs available. LinkedIn and Glassdoor are great places to start your search. Make sure that you have your portfolio ready to go and are prepared for the interview, which is what we’ll cover next. 

Ace the AI Engineering Interview

Once you’ve gained the skills and made a portfolio, you’ll eventually find yourself landing interviews for AI engineer roles. It’s essential that you get this final step right because it will decide whether you land your dream job or not. 

How do you prepare? We’ve put together lists of the most common artificial intelligence and machine learning interview questions for your benefit. Once you’ve worked on your broad skillset, you can move on to specifically preparing for these interview questions so that you know what you’ll be up against in the interview. 

Becoming an AI Engineer: Real-Life Examples

Here are a few people who went on the journey that you’re starting out on and have insights on how you can land your dream job in the best way possible. 

Nicolai Nielsen

Nicolai Nielsen is an AI engineer who works in the field of computer vision. He produces videos that give you a glimpse into the life of an AI engineer and also goes over technical topics like edge detection, image stitching, and object detection.

YouTube video player for n8QxPIceeN8

Jérome Crevoisier

Jérome Crevoisier is a great example for those who want to take the self-learners path to a career in artificial intelligence. In his Medium post, Jérome goes over the specific courses and projects he built to gain skills as an AI engineer.

how to become an ai engineer, Real-Life Examples, Jérome Crevoisier

How Much Can You Make as an AI Engineer?

Entry-Level AI Engineer

Entry-level AI engineers have an average salary of $70,317 annually.

how to become an ai engineer, Entry-Level AI Engineer average salary

Mid-Level AI Engineers

Salaries for mid-level AI engineers start off at around $108,000 and that’s the base income you can expect as a mid-career engineer.

how to become an ai engineer, Mid-Level AI Engineers average salary

Senior AI Engineer

Senior AI engineers make an average annual salary of $137,699.

how to become an ai engineer, Senior AI Engineer average salary

Resources To Find AI Engineering Jobs

Here are a few ways that you can go about finding jobs in the AI industry. 


LinkedIn has become the best place online to find jobs in any field. The best part about the job search on the site is that you can set up a job alert to notify you as soon as new jobs are posted for AI engineers. That way, you can stay up-to-date with openings without having to actively check the postings regularly. 

Job Boards

The following are job boards that post openings specifically in the AI industry:


As we said earlier, networking is key if you want to land a job in the AI industry. You should make sure that you’re regularly connecting with recruiters and professionals in the AI space, both online and in person. Make sure that you build these relationships over time so that you can turn to individuals in your network when you’re looking for a job. 

Slack Communities

Slack has become the go-to spot for members of the AI community to congregate online. Here are a few communities that you should consider joining:

How To Become an AI Engineer FAQs

We’ve got the answers to your most frequently asked questions:

Can You Become an AI Engineer Without a Degree?

Yes, it is possible to become an AI engineer without a degree. Companies now hire bootcamp graduates who are able to demonstrate experience in the field with their portfolio.

Does an AI Engineer Need To Know How To Code?

Not all AI engineers code. That said, it is important that you know the basics of coding so that you can solve problems programmatically when required. Python is a good beginner programming language if you want to work in AI.

Is AI Engineering Math-Heavy?

You do need to have a strong understanding of certain math concepts to work in AI. That includes linear algebra, calculus, probability, and statistics.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.