How to Become an Artificial Intelligence Engineer in 2024

How to become an AI Engineer- Understand the skills, roles, and responsibilities of AI engineers and the salary they earn to pursue a career in AI.

How to Become an Artificial Intelligence Engineer in 2024
 |  BY ProjectPro

The demand for data-related roles has increased massively in the past few years. Companies are actively seeking talent in these areas, and there is a huge market for individuals who can manipulate data, work with large databases and build machine learning algorithms.


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While data science is the most hyped-up career path in the data industry, it certainly isn't the only one. You can consider many other high-paying career options as a data enthusiast. This blog will take you through a relatively new career title in the data industry — AI Engineer.

 

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Why do you need to become an AI Engineer: Are AI Engineers in Demand?

A quick search on LinkedIn for AI engineering jobs in the world showed 30,267 results. This number indicates the rising demand for AI engineers in the industry. Top tech companies like Uber, Facebook, Google, IBM, Microsoft, etc. are hiring skilled AI Software Engineers and AI Research Engineers with lucrative AI engineer salaries throughout the year. If you're a fresher entering the industry or a software engineer looking to make a career transition, there is no better time than this to hone your artificial intelligence skills. 

AI Engineer Jobs on LinkedIn

 

The role of an AI engineer is relatively new in the data industry. Previously, companies would hire individuals with different areas of expertise — they would hire data scientists, data engineers, and machine learning engineers. These people would then work in different teams to build and deploy a scalable AI application. However, many AI-driven companies are starting to realize that these roles are highly intertwined. There are individuals skilled in all three — who can come up with AI solutions, scale, and deploy AI Models.

Individuals possessing all these skill sets are pretty rare and extremely valuable to organizations. That is one of the prime reasons for the huge demand for AI engineers, and there are an increasing number of job listings that require the skills of an AI engineer.

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What is an AI Engineer?

AI Engineers typically work to bring together the skills of a data engineer, data scientist, and software developer. This person can build and deploy complete, scalable Artificial Intelligence systems that an end-user can use. AI engineers build deep neural networks and machine learning algorithms to glean meaningful business insights based on the business goals they want to achieve. Artificial intelligence engineers are problem solvers who navigate between machine learning algorithmic implementations and software development.

What does an AI Engineer do?

An example of your day-to-day job as an AI engineer:

You are working for Company A, an Artificial Intelligence-driven company that builds products for its end users and staff. As an AI engineer, you and your data science team work on projects like building chatbots for the company's site. For this task, you will need to be able to work with large databases, extract data, and manipulate it. 

Then, you will require data science and machine learning skills to build the chatbot with available data. Finally, you will require the skills of a machine learning engineer to deploy this chatbot. You might need to use a cloud platform to do this, so in depth knowledge of these platforms is recommended. Upon deployment, customers and end-users will be able to use your chatbot.

As seen in the example above, an AI engineer brings together three important data science skills 

  1. The ability to handle large amounts of data (data engineering)

  2. The ability to build machine learning models (data science)

  3. The ability to deploy and scale these models (machine learning engineer). 

You can think of an AI engineer as a hybrid of these three data-related roles. This is a person who can produce end-to-end scalable machine-learning-applications that derives business value for the company.

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AI Engineer Roles and Responsibilities

The core day-to-day responsibilities of an AI engineer include -

  • Understand business requirements to propose novel artificial intelligence systems to be developed.

  • Design, develop, and deploy artificial intelligence systems.

  • Optimize system performance and enable the scalability of artificial intelligence Applications.

  • Evaluate and benchmark machine learning solutions to integrate them into existing systems.

  • Develop and implement Python or R-based API's.

AI Engineer vs. Machine Learning Engineer vs. Data Engineer 

As we mentioned above, the skills of an artificial intelligence engineer often overlap with that of machine learning engineers and data engineers. We will now describe the difference between these three different career titles, so you get a better understanding of them:

Data Engineer

A data engineer is a person who builds architecture for data storage. They can store large amounts of data in data processing systems and convert raw data into a usable format. After the data engineer formats data in a way that makes it easy to extract, data analysts and data scientists can easily query this data and build machine learning algorithms.

A data engineer is expected to be adept at using ETL (Extract, Transform and Load) tools and be able to work with both SQL and NoSQL databases. They should also be fluent in programming languages like Python and should know basic shell scripting in Unix and Linux. These individuals make data accessible to everybody else in the company and build a platform that allows others to pull out data efficiently. Data engineers should also possess practical knowledge using diverse cloud platforms like AWS, Azure or GCP.

Machine Learning Engineer

A machine learning engineer is someone who puts artificial intelligence models into production. These individuals make the connection between data and software.

If you are a beginner in the data industry, you have likely built machine learning (ML) models in your Jupyter Notebook. Have you ever thought about getting this model into the hands of an end user?

For example:

Let's say you build a movie recommendation system using Netflix's database of movie streamers. Have you thought about what happens when more data comes in? How will your model be able to automate and send recommendations as more subscribers join the platform? How will your model learn and scale as users watch more movies?

In organizations, the models built by data scientists need to reach the end users. These models need to be deployed. They also need to be scaled, meaning that they should be able to process large amounts of data and come up with predictions quickly. ML engineers will put models into production such that large amounts of data can be collected and processed in a short amount of time.These individuals need to have strong programming and software engineering skills. They should be familiar with programming languages like Python, Java, and C++. Furthermore, they should also have an understanding of ML frameworks like Keras, Tensorflow, and Pytorch. Finally, these individuals need to have a strong command of using automation technologies and should be able to deploy models on cloud platforms like AWS.

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Artificial Intelligence Engineer

An AI engineer is a person who is able to come up with an end-to-end workflow for productionizing AI systems. This person should have some data engineer skills and the skill set of a data scientist and a machine learning engineer. As an AI engineer, you should be able to structure raw data and convert it into a usable format. Then, after building ML models, you should be able to build and scale these models. These are skills that data engineers and ML engineers possess. In a nutshell, AI engineers are individuals who are can build and deploy scalable AI products that end-users can access.

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AI Engineer vs Data Scientist

Most people confuse an AI Engineer with a data scientist. However, the two roles are different from each other. Usually, a data scientist role involves the utilization of statistics, mathematics, design, and communication skills to solve a business problem. Additionally, the role involves the deployment of machine learning/deep learning problem solutions over the cloud using tools like Hadoop, Spark, etc. On the other hand, an AI Engineer comes with an all-in-one package of skills to transfer human intelligence to machines. They are responsible for building robust AI-based systems utlizing deep learning algorithms, machine learning models, NLP, and computer vision. 

Skills Required to Become an AI Engineer

To become an AI engineer, you will need to possess the following technical skills:

  • Learn programming languages - Proficiency in object-oriented programming languages like Python, C#, or C++ is needed to become an AI engineer. 

  • An understanding of frameworks like Keras and Tensorflow to build AI solutions

  • Ability to build deep learning algorithms using neural networks — familiarity with CNNs and RNNs

  • Familiarity with cloud platforms like Google Cloud, Amazon AWS, or Microsoft Azure — you should be able to deploy and scale models using these platforms.

  • An understanding of software development methodologies like Agile or Scrum

  • Experience with cloud computing

  • Knowledge of Data science, data wrangling, and big data 

With the technical skills above, you will be able to build AI solutions and deploy them. However, to work with cross-functional teams in organizations as an artificial intelligence engineer, the non-technical skills you should possess are -

  • Good communication skills — You should be able to communicate well and present business insights based on the machine learning models you built.

  • The ability to match a business requirement to the end product — The AI applications you build need to have business value. You should have the ability to tie back the company's business requirements to the models you build and make sure your models generate revenue for the company.

  • Ability to work in a team — As an AI engineer, your team members will come from very different backgrounds. Some might be better software engineers, and some might be better at building machine learning models. You should have the ability to work with people from these different backgrounds to come up with a scalable end product.

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What degree do you need to be an AI Engineer?

AI engineers can come from very diverse backgrounds. The skills possessed by an AI engineer are highly different from each other. As an AI engineer, you should be skilled in statistics/mathematics and building machine learning models, and at the same time, have strong technical and programming knowledge. Due to this, most AI engineers come from one of the following backgrounds:

  • Computer science

  • Software engineering

  • Data science

  • Statistics

  • Mathematics

However, suppose you don't come from one of these educational backgrounds or don't have a quantitative degree. In that case, you can learn all the skills required to become an AI engineer on your own by working on some interesting AI projects and getting a hands-on experience of what the role of an Artificial Intelligence Engineer entails. While it is a bonus to have a quantitative degree in fields like computer science and statistics, it isn't necessary. One can also land a job as a self-taught Artificial Intelligence engineer, as long as they learn all the machine learning tools and technologies mentioned above and have strong statistical knowledge and programming skills. It is possible to teach yourself all these Artificial Intelligence skills from scratch. In the next section, we will break down the different steps you can take to teach yourself the skills required to become an Artificial Intelligence engineer.

How to Become an AI Engineer: AI Engineer Roadmap

AI Engineer Skills

 

To become an AI engineer, you need to possess the skill set outlined above.  This section will discuss what to study to become Artificial Intelligence engineer. To become an AI Engineer, you will need to learn the following:

Machine Learning

First, you will need to gain an understanding of machine learning. Learn the different types of supervised and unsupervised machine learning techniques and with their implementation.

Machine Learning for becoming an AI engineer

 

When starting, make sure to learn basic algorithms like linear regression, logistic regression, and decision trees first. An understanding of these algorithms is crucial when making the transition into deep learning. Learn either Python or R, and gain an understanding of packages like Scikit-Learn and Keras.

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Statistics

You will need to understand the basics of statistics in order to learn how these algorithms work. Learn the different types of probability distributions, along with topics like hypothesis testing and sampling. A solid statistical foundation will help you to build machine learning models and aid with feature selection and data pre-processing. Once you learn statistics and machine learning and can to implement the concepts you learned in Python, try practicing diverse data science and machine learning projects

Statistics for becoming an AI engineer

 

Work with diverse machine learning datasets to apply the concepts you learned in real-life situations. This will help you better understand topics like feature selection and data standardization. Knowledge of mathematical concepts, specifically calculus and linear algebra, will be useful.

Deep Learning

You will also need to understand different deep learning algorithms that can be used to build AI applications. Master the art of building and training neural networks for tasks like natural language processing and computer vision. You need to learn about the three popular deep learning algorithmsANNs (Artificial Neural Networks), CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks)

Deep Learning for becoming an AI engineer

 

After you learn the theory behind these algorithms and understand how they work, you should start to implement them. To do this, you will need to work with packages like Keras, Tensorflow, and Pytorch.It would be best if you spend some time learning these deep learning frameworks, particularly Tensorflow, as it is the most widely used framework that AI engineers work with.

Model Deployment

After learning the topics mentioned above, you will can build AI applications from scratch. Now, you need to be able to deploy these applications and scale them. To do this, you need to learn how to put models in production with popular cloud platforms — Google Cloud, Amazon AWS, and Microsoft Azure. Pick any of these three platforms and start learning how to deploy models on them. Cloud computing is an essential skill for an AI engineer to have, so make sure to gain as much experience as possible in using these platforms.

Model Deployment for becoming an AI engineer

Other AI Skills

You will need to be well versed with programming, particularly object-oriented programming (OOP). Learn how to code in Python, Java, C++, or any other OOP language.

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Then, gain an understanding of how to work with shell scripting. To do that, you can download a virtual machine like Ubuntu on your device and learn how to work with the Linux command line. Finally, you should learn to work with large databases. To become an AI engineer, you also need to have a fundamental understanding of database creation and design and must know how to build data pipelines. Knowledge of SQL queries to manipulate data is also essential for an AI engineer.

Software engineers generally fare very well as AI engineers. This is because they have experience in building and deploying full-stack web applications, which is a massive part of what AI engineers do. If you're transitioning from software development/computer engineering to artificial intelligence, you might find the transition into AI engineering a lot easier than most people.

How long does it take to become an AI Engineer?

The time taken by an individual to become an AI Engineer from scratch depends on their educational background. Suppose they have pursued computer engineering in their graduation years.In that case, it will take them less time to hone the appropriate skills than an individual without a degree in computer science. Additionally, it depends on how much time one is investing in understanding various concepts in Artificial Intelligence engineering.  

Who should become an AI engineer?

If you are a person with interest in the field of machine learning and enjoy programming, then this career option might be suitable for you. The competition isn't very high to become an AI engineer. Fields like software engineering and data science are highly competitive, and the job market is saturated with individuals looking to land a job in these industries. However, because AI engineers are required to have a skill set that includes software engineering and other data-related roles, the barrier of entry is slightly higher.

Statisticians and data scientists can't become AI engineers without knowing how to manipulate data and deploy machine learning models. Software engineers can't become AI engineers without knowing statistics and deep learning. Due to this, there are a lower number of people who qualify for jobs as AI engineers, meaning that there is less competition for AI engineer jobs.

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AI Engineer Salary -How much does an AI Engineer make?

So, what's the median AI engineer salary? The average AI engineer salary in the US is around $165K. This salary is higher than the median income of a data scientist, which is approximately $130K. It is also higher than the income of a software engineer, which is around $100K at an intermediate level. Like any other job role, the AI engineer salaries vary based on the location, industry, educational qualification, and company.

How can ProjectPro Help You Build a Career in AI?

The role of an AI engineer is relatively new in the data industry, but it is here to stay. There is a massive demand in the industry for individuals who possess the skills required to deploy scalable AI applications. Companies of all sizes (from small start-ups to large organizations like Sony and Microsoft) hire AI engineers to build machine learning products. Another advantage of taking a job as an AI engineer is that if you want, you can easily make the transition into fields like data science, data engineering, software engineering, and machine learning engineering. Also, the pay scale of an AI engineer is a lot higher than that of a software engineer and is also higher than that of a data scientist.

If you are interested in becoming an AI engineer, you can learn all the skills required by yourself by practicing diverse machine learning and data science projects. These AI projects have been vetted and created along with industry experts from Uber, JPMorgan, Paypal to ensure relevance in the AI industry and provide the content that matters the most for mastering AI skills.

FAQs on Becoming an AI Engineer

Is AI engineer a good career?

Yes, AI engineer is a good career and is considered as one of the most well-paid job in the world. Furthermore, many top-notch companies like Google and Microsoft are looking forward to hiring AI Engineers.

What is the salary of a AI engineer?

As per Glassdoor, the AI engineer’s salary in India is about ₹9,44075 per year. And, an AI engineer's salary in the US is $1,20,384 per year.

Is AI engineering hard?

No, AI engineering is not hard. If you learn about AI engineering from the right resources, starting a career in AI engineering won't seem challenging.

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