Who Is an AI Engineer? (And How to Become One)

Artificial intelligence (AI) is a branch of computer science that involves programming machines to think like humans. While simulating human actions might sound like the stuff of science fiction novels, it is a tool that enables us to rethink how we use, analyse, and integrate information to improve business decisions. AI has great potential for finance, national security, healthcare, criminal justice, and transportation.

According to PricewaterhouseCoopers' Global Artificial Intelligence Study [1], by 2030, AI could contribute up to $15.7 trillion to the global economy, more than China and India’s combined output today. This projected growth means organisations will turn to AI to help power their business decisions and increase efficiency. 

For an AI engineer, that means plenty of growth potential and a healthy salary. Read on to learn more about what an AI engineer does and how to get started.

What is AI engineering?

AI engineers use AI and machine learning (ML) techniques to develop applications and systems to help organisations increase efficiency, cut costs, increase profits, and make better business decisions.

AI engineering focuses on developing the tools, systems, and processes that enable artificial intelligence to be applied in the real world. Any application where machines mimic human functions, such as solving problems and learning, can be considered artificial intelligence. Algorithms are “trained” by data, which helps them to learn and perform better.

You may have encountered the results of AI engineering when you use Netflix, Spotify, or YouTube, where machine learning makes customised suggestions based on your behaviour. Another popular example is transportation, where AI and machine learning technology drive self-driving cars. AI is instrumental in healthcare because it can power robots to perform surgery and generate automated image diagnoses. 

Is AI for me?

The course AI for Everyone breaks down AI to be accessible for those who might not need to understand its technical side. If you want a crash course in the fundamentals, this class can help you understand key concepts and spot opportunities to apply AI in your organisation.

AI engineer responsibilities

AI engineers play an essential role in organisations that use AI. They chart the AI strategy and define the problems to be solved with AI. They’re in charge of building and implementing AI development and production infrastructure. Here are some of an AI engineer’s specific tasks and responsibilities:

• Create and manage the AI development and production infrastructure

• Conduct statistical analysis and interpret the results to guide and optimise the organisation’s decision-making process

• Automate AI infrastructures for the data science team

• Build AI models from scratch and help product managers and other stakeholders with analysis and implementation

• Transform ML models into APIs that integrate with other applications

• Collaborate across teams to help with AI adoption and best practices 

Salary and job outlook

AI engineering is a specialised field that has promising job growth and tends to pay well. 

According to Talent, the median base salary for an AI engineer is ₹1,800,000 in India [2]. 

India Today reports a promising job outlook for AI engineers. New roles could open for junior and senior researchers, statisticians, and practitioners [3].

AI engineers typically work for companies like Google, IBM, and Meta, helping them improve products, software, operations, and delivery. They may also work in government and research facilities that aim to improve public services. 

How to become an AI engineer

Becoming an AI engineer requires basic computer, information technology (IT), and maths skills, which are critical to guiding AI programs. Here’s how you can get started in AI engineering.

1. Learn the skills needed. 

You’ll need to build your technical skills, including knowledge of the tools that AI engineers typically use. 

• Programming: You’ll want to learn programming languages such as Python, R, Java, and C++ so you can build and implement models.

• Probability, statistics, and linear algebra: AI engineers need these skills to implement different AI and ML models.

• Big data technologies: AI engineers work with large amounts of data, so you must know Apache Spark, Hadoop, and MongoDB to manage it all.

• Algorithms and frameworks: You’ll want to understand ML algorithms, such as linear regression and Naive Bayes, and deep learning algorithms, like recurrent neural networks and generative adversarial networks. You must also be able to implement them with a framework. Common AI frameworks include Theano, TensorFlow, Caffe, Keras, and PyTorch.

You can learn these skills through online courses or bootcamps to help you launch your AI career. 

Do I need a degree to become an AI engineer?

No, you don’t need a degree to become an AI engineer. People increasingly use professional certificate programmes to learn the necessary skills and interview preparation. 

Glassdoor reports that 65% of AI engineers in India have a bachelor’s degree, and some companies may even require a master’s degree [4]. Common majors include computer science and information technology. Jobs in AI are competitive, but if you can demonstrate you have a strong skill set and do your interview well, you can launch your AI engineer career.

2. Apply for jobs.

Applying for a job can be intimidating when you have little or no experience, but it might be helpful to know people are hired daily for jobs that need experience. There are many ways to enhance your resume for job search success. For AI engineering jobs, you’ll want to highlight specific projects you’ve worked on for jobs or classes demonstrating your broad understanding of AI engineering.

Once you’ve applied for jobs, you’ll hopefully get an interview. Don’t be discouraged if you apply for dozens of jobs and don’t hear back—data science is an in-demand (and lucrative) career field, and companies can receive hundreds of applications for one job.

Preparing for the interview requires practice and preparation, especially for tech jobs in AI engineering. You’ll want to brush up on your interview skills to show hiring managers you’re perfect for the job. You must explain your reasoning for developing, deploying, and scaling specific algorithms. These interviews can get very technical, so be sure you can demonstrate how you solved a problem and why you chose to solve it that way.

3. Get work experience.

Even an entry-level position or internship may have several interview rounds. But if you land a job, it’s time to prove yourself and learn as much as possible. You can then apply the skills you learned to delivering business insights and solutions that can change people’s lives, whether it is in healthcare, entertainment, transportation, or consumer product manufacturing.

Explore artificial intelligence with Coursera

Launch your career as an AI engineer with IBM’s AI Engineer Professional Certificate. You’ll learn to generate business insights from big data using ML techniques and gain essential skills to deploy algorithms with Apache Spark and models and neural networks with Keras, PyTorch, and TensorFlow. Start today and finish in nine months or less.