What is an Artificial Intelligence Engineer? What do they do?

Ten years ago, the idea of computer software programs that could develop their own intelligence seemed like science fiction. Now, it has become an integral part of the social media and search engines we use every day. Artificial intelligence tells us where we should go when we don't know the way and can even offer new movie recommendations to watch based on the genres we watch the most.

Artificial intelligence (AI) is generally the development and training of computers to simulate and perform tasks that typically require human intelligence. In short, the goal of artificial intelligence is to enable machines (such as computers) to act like a person with human-like intelligence.

Statista predicts that by 2023 there will be 8 billion AI-powered voice assistants. As you know, since the launch of OpenAI's ChatGPT' in 2022, every company of all sizes has been trying to find ways to use AI applications to make their businesses more efficient, effective, and profitable.

All of this signifies that the need for AI engineers is rapidly increasing. In this article, we explore what an AI engineer is, some of their responsibilities, and the steps you can take to become an AI engineer.

What is Artificial Intelligence?

Artificial intelligence uses computers and machines to mimic how the human mind works in order to perform problem-solving and decision-making tasks.

In artificial intelligence, machines learn the outcomes of actions. They process data to do so. Then, they use the information obtained from this process to make decisions about future actions and to solve problems. At the same time, data related to the machine's decisions is collected and used to improve future actions and decisions.

What is an AI Engineer?

Whenever we use Netflix, Spotify, or YouTube, we encounter the work of AI engineers in the recommendations of machine learning. An AI engineer is a professional 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.

An AI engineer develops the software architecture necessary to train algorithms to be "intelligent" and then uses them to solve specific problems. They must identify areas where a company's operations can benefit most from AI solutions.

An AI engineer is typically an advanced type of machine learning engineer or data scientist. Some may have a different academic background, but everyone needs to interact with the world of data and machine learning at some point. If you are curious about the future of AI engineering, you can read our article.

Responsibilities of an AI Engineer

AI engineers play a significant role in organizations based on artificial intelligence. They are not only responsible for identifying problems that can be solved using AI but also for developing, producing, and implementing AI systems. Some of their other tasks include:

1. Establishing and managing the AI development process and overall infrastructure of a product
2. Conducting statistical analysis and interpreting results to guide an organization's decision-making process
3. Automating infrastructure for the data science team
4. Developing infrastructures for data transformation and acquisition
5. Creating AI models that make predictions based on large amounts of data
6. Converting machine learning models into APIs to interact with other applications
7. Coordinating with software teams to identify which business processes can be improved using AI
8. Implementing machine learning techniques for image recognition
9. Creating and maintaining chatbots that interact with customers
10. Developing AI-focused solutions that mimic human behavior to perform repetitive tasks currently done by humans
11. Creating recommendation engines for e-commerce sites, streaming services, and other applications

Skills Required to Become an AI Engineer

Artificial intelligence is a broad field, and an AI engineer requires the skills of both a software engineer and a data scientist. In fact, knowledge of mathematics and statistics can even be helpful.

1. Programming Languages

The first skill required to become an AI engineer is programming. To be knowledgeable in AI, it is essential to learn programming languages such as Python, R, Java and C++ is very important to learn such programming languages. 

Increasing use of Python over the years - Source

Python is popular due to its easy-to-learn syntax and comprehensive libraries for artificial intelligence and machine learning. Knowing these languages is very important for developing algorithms, applying models, and working with data.

TensorFlow, Keras and PyTorch are commonly used libraries for AI development in Python. Examples of their use include predictive analyses, natural language processing, and image recognition.

2. Data Modeling 

Data is the foundation of artificial intelligence, and AI engineers need to have an in-depth knowledge of data modeling and engineering. They need to know how to acquire data, clean it, and convert it into a suitable format for analysis. 

To query and manage large datasets, knowledge of databases such as SQL and NoSQL databases are required. Tools such as Apache Spark, Hadoop and AWS S3 are also commonly used tools for data processing in AI projects.

3. Big Data Analysis

AI engineers must be able to analyze large datasets and extract meaningful insights from them. This includes using big data tools such as SparkSQL, Apache Flink and Google Cloud Platform, among other big data tools.

4. Machine Learning Models

Knowledge of machine learning models and algorithms is essential 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).

5. AI and Machine Learning 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 cloud-based AI and machine learning services provide pre-built models, APIs, and tools for developing intelligent applications without extensive coding.

6. AI Deployment and DevOps

Deploying AI applications requires collaborating with DevOps teams to ensure seamless integration and deployment. AI engineers should know how to use tools like Docker and Kubernetes to manage containerization, configuration management, and deployments. Additionally, they are responsible for monitoring and maintaining the performance of AI systems post-deployment.

7. AI Security

As AI applications bring new security vulnerabilities, understanding and implementing strong data security and privacy measures is crucial for every AI engineer. As an AI engineer, it is your responsibility to ensure the confidentiality, integrity, and availability of the data you use.

Additionally, AI engineers must be knowledgeable about various encryption methods, developing secure AI models applications and must be prepared to handle potential security attacks or breaches.

Some tools used to secure AI applications include:

• AWS Identity and Access Management: A service that allows you to securely manage access to AWS resources
• TensorFlow Privacy: A library that facilitates the development of secure and privacy-preserving machine learning models

How to Become an AI Engineer?

1. Gain Programming Experience

Improve your skills in programming languages such as Python, R, or Java. Practice coding and work on projects that involve data analysis and machine learning. 

2. Learn Machine Learning and Artificial Intelligence 

To understand the fields of machine learning and artificial intelligence well, you first need to have a basic knowledge of mathematics (probability, statistics, linear algebra) and programming.

You can start AI and machine learning courses offered on platforms like Coursera and Udemy. Additionally, courses that teach libraries such as TensorFlow or PyTorch will also be beneficial. By reviewing data science and AI interview questions , you can also address any gaps in your knowledge.

3. Work on Projects

The best way to reinforce what you've learned is by working on real projects. Platforms like Kaggle offer opportunities to work with real-world data and interact with the community. Build your portfolio by creating applications or systems that showcase your AI skills. Collaborate with others on open-source projects to gain practical experience.

4. Stay Current and Join Communities

AI is a rapidly evolving field, so staying up-to-date with the latest developments and trends is very important. Follow blogs and Discord channels related to AI. Attend conferences or meetups to expand your knowledge. Through our Discord channel, you can connect with AI enthusiasts and experts and follow the latest news through our monthly developer newsletter.

5. Participate in Competitions and Hackathons

Participate in AI competitions, hackathons and data science competitions. Platforms like Kaggle offer a wide range of datasets and competitions where you can showcase your skills and gain insights from community feedback. To learn more about what Kaggle is, you can read our article.

6. Gain Practical Experience

Look for internships, research opportunities, or entry-level positions in companies focused on AI. The practical experience you gain while working on real-world projects will help you apply your theoretical knowledge and develop your skills.

7. Emphasize Continuous Learning

Artificial intelligence is a constantly evolving field, so embracing lifelong learning is very important. Stay curious, explore new algorithms, techniques, and tools. Experiment with different datasets and learn from your successes and failures.

The Future of AI Engineering

Just think of all the developments in artificial intelligence over the past ten years: smart home devices, driverless cars, facial recognition robots, and ChatGPT... The list goes on. AI has revolutionized every field from healthcare to production, and from manufacturing to the arts in just a few years.

As artificial intelligence becomes more widespread, the need for AI engineers in companies increases and is expected to continue rising in the future. There are quite a few career opportunities for those who want to work in AI research and development.

AI bootcamps and the increase in training programs are leading more and more people to pursue careers in this field. If you want to advance in the field of AI in your career but don't know where to start, you can ask your questions in our Discord channel composed of technology workers.