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The 13 Essential AI Engineer Skills You Need to Know

AI engineer skills are now in demand. Learn all about the essential skills needed in this complete guide.
Dec 2023

With job postings involving AI doubling over the past two years, an AI engineer career is no exception.

But what are the skills used by an AI engineer, and what should you learn if you plan to become one?

In this article, we'll provide an overview of the top 9 essential AI engineer skills that you need to know.

Developing skills in AI engineering is crucial for implementing machine learning systems. So, if that's what you're interested in, do read on! If you’re looking to learn AI from scratch, check out our separate guide.

What is an AI Engineer?

AI engineers are responsible for designing, developing, and maintaining AI-based systems. They use their skills in machine learning, programming, data analytics, and other technologies to create intelligent applications that can learn from data and make decisions.

AI Engineer Technical Skills

To better understand the role of an AI engineer, let's look at the technical skills they typically possess:

1. Programming languages

As it goes without saying, an AI engineer needs to be proficient in programming languages.

Some of the commonly used languages for AI development include:

  • Python
  • Java
  • R
  • C++
  • JavaScript


Python is especially popular due to its easy-to-learn syntax and extensive libraries for AI and machine learning. Knowledge of these languages is crucial for developing algorithms, implementing models, and working with data.

increase in Python use

Increase in use of Python over the years - Source

This has led to its increase in popularity over the years, according to the TIOBE index.

Libraries such as TensorFlow, Keras, and PyTorch are commonly used in Python for AI development. Use cases include predictive analytics, natural language processing, and image recognition. Start learning today with our Python Programming track.

Want to use PyTorch for deep learning? Kick-start your learning using our cheat sheet below.

Deep Learning with PyTorch Cheat Sheet

Our Deep Learning with PyTorch Cheat Sheet


R language, primarily used for statistical analysis and graphics representation, is also frequently used in AI.

The 'caret' package in R aids in predictive modeling, while 'e1071' simplifies the process of creating a machine learning model. R finds its use in AI projects related to scientific research, healthcare, finance, and social media analytics.

We have an R Programming skill track, which will teach you how to code like a programmer from scratch.


Java is another popular choice in AI development, mainly due to its simplicity, readability, and excellent user interaction capabilities.

Its strong memory management and the breadth of high-quality machine learning libraries, such as Stanford Library NLP and Apache Open NLP, make it suitable for chatbot development.

Use cases include chatbots, website recommendation systems, and fraud detection in the finance sector.

If you plan to use the OpenAI API for chatbot development, take a look at our course on Working with the OpenAI API.


C++ provides the ability to execute high-level applications with a relatively low computational cost, making it suitable for machine learning and neural network computation.

Libraries like Shark and mlpack are designed for machine learning in C++. AI use cases include computer vision tasks, robotics, and gaming. Check out our tutorial on Llama.cpp to learn how C++ is used with the LLaMa LLM.

2. Data modeling & engineering

Data is the foundation of AI, and AI engineers need to have a deep understanding of data modeling and engineering.

They should know how to acquire, clean, and transform data into a suitable format for analysis. Knowledge of SQL and NoSQL databases is essential for querying and managing large datasets.

Tools such as Apache Spark, Hadoop, and AWS S3 are also commonly used for data processing in AI projects.

3. Big data analysis

AI engineers should be able to analyze large sets of data and extract meaningful insights from them. This involves using big data tools such as SparkSQL, Apache Flink, and Google Cloud Platform to query and manipulate large datasets.

Our big data fundamentals with PySpark course covers many of the principles behind big data analysis, making it a perfect starting point for you to learn.

4. Machine learning models

Knowledge of machine learning models and algorithms is a must for AI engineers.

This includes understanding supervised and unsupervised learning techniques, as well as deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

AI engineers should also know how to evaluate the performance of these models using metrics like

  • Accuracy
  • Precision
  • Recall
  • Root-mean-square deviation (RMSE),
  • Mean absolute error (MAE)
  • F1 score

Our Machine Learning Fundamentals with Python skill track can help you learn many of the basics of machine learning.

5. AI and ML services

AI engineers should also be familiar with cloud-based AI and machine learning services offered by major providers such as AWS, Azure, and Google Cloud Platform. These services provide pre-built models, APIs, and tools for developing intelligent applications without the need for extensive coding. Check out out AWS, Azure and GCP Service Comparison cheat sheet for more details. 

6. AI Deployment & DevOps

Deploying AI applications involves collaborating with DevOps teams to ensure smooth integration and deployment.

AI engineers should know how to use tools such as Docker and Kubernetes for containerization, configuration management, and orchestrating deployments.

They are also responsible for monitoring and maintaining the performance of AI systems post-deployment.

Our Introduction to Docker course provides a good foundation to start with, and you can find out more about Docker certification in a separate article.

For guidance on how to implement AI, you can check out our course on Implementing AI Solutions in Business.

7. AI security

Understanding and implementing strong data security and privacy measures is crucial for any AI engineer, as AI applications bring new security vulnerabilities.

As an AI engineer, it is your responsibility to ensure the confidentiality, integrity, and availability of the data you handle.

This involves understanding regulations related to data protection, such as the General Data Protection Regulation (GDPR), and implementing secure AI frameworks that comply with these regulations.

Additionally, AI engineers must be knowledgeable about various encryption methods, secure AI model development practices, and be prepared to handle potential security attacks or breaches.

Some techniques used by AI engineers include:

Some tools used for securing AI applications include:

To help you get started, I would recommend having a look at Google’s Secure AI Framework.

Non-Technical Skills for AI Engineers

Apart from technical skills, AI engineers should also possess certain soft skills to excel in their roles. These include:

8. Communication and collaboration

AI engineers need strong communication skills to effectively communicate complex AI concepts and ideas to non-technical stakeholders.

Moreover, as AI projects are often team efforts, collaboration is essential for efficient project execution.

You'll likely be in constant meetings with other tech, such as:

  • Data scientists, to discuss model development and optimization
  • Data analysts, to understand data requirements for AI projects
  • Software developers, for integration of AI models into existing systems
  • Project managers, to ensure timely and successful project delivery

9. Adaptability and continuous learning

The field of AI is constantly evolving, with new tools, techniques, and advancements emerging all the time.

Therefore, AI engineers must be adaptable and willing to continuously learn to stay up-to-date with the latest developments in the field.

10. Critical thinking and problem-solving

The ability to think critically and solve complex problems is vital for AI engineers.

AI projects often involve working with large datasets, developing sophisticated algorithms, and troubleshooting issues that arise during model development or deployment.

11. Domain knowledge

Having domain knowledge in your specific area of focus can give you an edge as an AI engineer.

For example, if you work on healthcare-related AI projects, having a strong understanding of the healthcare industry and its challenges can help you develop more effective solutions.

Similarly, if you work on financial AI projects, having a background in finance or economics can be beneficial.

Advanced Skills for AI Engineering

12. Advanced mathematics and algorithm knowledge

Advanced mathematics, such as linear algebra, calculus, and statistics, are essential for understanding AI algorithms and techniques.

Here’s a statistics cheat sheet to help you get started:

Our Descriptive Statistics Cheat Sheet

AI engineers should also know how to develop custom algorithms based on these mathematical models to solve unique problems.

Our Statistics Fundamentals with Python course helps you develop some of the essential skills if you're new to the space.

13. Neural network architectures

Understanding and applying neural networks in AI solutions is a critical skill for AI engineers.

This involves knowing the different types of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), their applications, and how to implement them.

Some tools used for developing neural network architectures include:

  • TensorFlow: an open-source library for building and training neural networks
  • Keras: a high-level API that runs on top of TensorFlow to simplify the process of building, testing, and deploying deep learning models

Where to Learn AI Engineer Skills

To start building up on all these skills, you can start with:

1. Work on projects

If you are already working in a tech-related role, look for opportunities to collaborate with AI teams or work on AI projects. This will give you hands-on experience and help you understand the specific skills required for AI engineering.

Additionally, I would recommend looking at some machine learning projects that involve deep learning as well.

2. Online courses and tutorials

There are plenty of online courses and tutorials available that cover various AI engineering skills. We’ve already linked to many of these throughout the article, but if you're interested in getting started, check out our AI Fundamentals Skill Track.

3. Attend AI conferences and workshops

Attending AI conferences and workshops can give you a chance to network with other professionals in the field, learn about new tools and techniques, and gain valuable insights into industry trends.

For webinars, check out this one on Designing Data & AI Products. For a full conference, you might like our RADAR AI conference.

4. Read industry publications

Staying updated on the latest developments in the AI industry is crucial for AI engineers.

You can source them from resource portals like:

  • arxiv: a free online repository of research papers in various fields, including artificial intelligence and machine learning.
  • MIT Technology Review: a magazine focused on emerging technologies and their impact on society.

Wrapping Up

AI engineering is a rapidly growing field with immense potential for those who possess the necessary skills and knowledge.

With the right combination of technical and non-technical skills, you can excel in this role and contribute to advancing groundbreaking AI solutions.

Thinking of picking up these AI engineer skills for yourself? To get started in the field of AI, check out our AI Fundamentals Skill Track or selection of AI Tutorials.

Photo of Austin Chia
Austin Chia

I'm Austin, a blogger and tech writer with years of experience both as a data scientist and a data analyst in healthcare. Starting my tech journey with a background in biology, I now help others make the same transition through my tech blog. My passion for technology has led me to my writing contributions to dozens of SaaS companies, inspiring others and sharing my experiences.


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