How to Become an AI Engineer

If you enjoy problem-solving, have an analytical mind, and possess good computer and math skills, you might want to learn how to become an AI engineer. In this role, you’ll develop artificial intelligence (AI) models and applications capable of thinking like humans, and when it comes to lucrative fields, few compare to AI. Growth in this market is set to reach $1.8 trillion by 2030 [1]. Explore AI engineering in detail, including common responsibilities, salary, and job outlook, and get tips for pursuing this exciting career path. 

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Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

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Logistic Regression, Artificial Neural Network, Linear Regression, Decision Trees, Recommender Systems, Tensorflow, Advice for Model Development, Xgboost, Tree Ensembles, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Supervised Learning, Anomaly Detection, Unsupervised Learning, Reinforcement Learning, Collaborative Filtering

What is AI engineering?

AI engineering involves using a variety of skill sets—from computer programming and data analysis to machine learning (ML)—to develop intelligent systems capable of performing tasks for humans. These tasks might involve solving problems beyond normal human intelligence, performing tasks that put humans at risk or doing jobs that humans find undesirable. 

Examples of intelligent systems that AI engineers build include:

• Customer service chatbots

• Robots for various tasks like entertainment, manufacturing, and medical surgery 

• Self-driving vehicles

• Systems that predict maintenance for automated equipment

• Systems that predict customer preferences

• Surveillance drones

What does an AI engineer do?

Although your tasks may differ depending on your employer, some common duties and responsibilities you could have as an AI engineer are:

• Developing, testing, and training AI models

• Conducting data analysis to improve AI function 

• Performing maintenance on AI models 

• Collaborating with other team members like data analysts and software developers

• Working with AI users and product managers to ensure the proper function of systems

Artificial intelligence engineer salary and job outlook

As an AI engineer, you can typically make an average salary of $112,693 per year. This figure reflects a base salary without added bonuses or profit sharing [2]. 

The future job outlook appears bright for computer and information technology professions of all kinds. Faster-than-average job growth is likely for computer and IT jobs as a whole through 2033 [3]. 

How to become an AI engineer: 5 key strategies

Knowing what educational path to follow, what skills and experience you’ll need, and how to gain knowledge can help you determine if an AI engineering career path works for you. To start the process, consider five key strategies. 

1. Pursue a bachelor’s degree.

The first step toward becoming an AI engineer typically involves pursuing a bachelor’s degree. Good majors to consider include computer engineering, computer science, data science, or software development. Types of courses you can expect in these majors include:

• Calculus, physics, and chemistry

• Computer programming

• Electrical circuits

• Engineering design

• Data analysis

• Machine learning

• Statistics

• Software design

If you want to boost your marketability in this field, you may choose to continue your education and earn a master’s degree in AI. Also, as part of the AI engineer education requirements, some positions may demand a master’s degree.

2. Get AI-related credentials or participate in boot camps.

Participating in boot camps or getting AI-related credentials can help you gain the knowledge and skills you’ll need for a career in AI engineering. In addition, an entry of “certified AI engineer” on your resume might set you apart from other job candidates with some employers.

A few examples of top programs include:

• AI Boot Camp from Columbia University

• Certified Artificial Intelligence Engineer (CAIE) from the United States Artificial Intelligence Institute (USAII)

• Microsoft Azure AI Engineer Associate

• IBM AI Engineering Professional Certificate

3. Build your computer programming skills. 

Building your computer programming skills enhances your prospects for any job in AI. Below, find some useful programming languages for AI engineers and other professionals:

• Java: Provides quick processing and computation, known for its portability (code works the same on any device)

• Python: Simplicity, wide array of libraries and frameworks, and integrates well with other programming languages

• R: Often preferred for projects involving data visualization and deep statistical analysis

Additional AI engineering skills

AI engineer roles require a variety of skills in addition to knowledge of programming languages. Examples of important AI engineering workplace skills include:

• Collaboration

• Communication

• Analytical thinking

• Critical thinking

• Mathematical abilities

• Problem-solving 

4. Gain practical work experience.

Having practical work experience in your desired field can make you a more marketable job candidate. To gain valuable experience for an AI engineering position, consider these tips:

• Apply for an AI engineering internship at a local engineering firm.

• Take entry-level positions working in data analytics or software development.

• Sign up for AI competitions or contribute to open source AI projects.

5. Build a digital portfolio of your work.

A digital portfolio of your work can help an employer see what you might offer as a future employee. Additions to your portfolio might include case studies, research papers, AI projects you’ve participated in, or AI models you’ve created.

AI engineer career path

Knowing about potential career paths in the AI engineering field can help you learn what your future may look like. When considering an AI engineering career, remember that you have a variety of options for AI engineering positions, including:

• AI software engineer

• Computer vision engineer

• Machine learning engineer

• Natural language processing (NLP) engineer

• Robotics engineer

The following career path examples indicate possibilities for entry-level, mid-level, and leadership positions you might have.

AI software engineer→senior AI engineer→chief AI officer

Machine learning engineer→AI researcher→AI director

Build AI engineering skills and knowledge on Coursera

Artificial intelligence adds a level of growth, innovation, and excitement to many career fields, including engineering. 

Once you’ve learned how to become an AI engineer, you can further solidify your career interest by taking an online course on Coursera. Consider the Machine Learning Specialization by Stanford University and DeepLearning.AI or the IBM DevOps and Software Engineering Professional Certificate. Both designed for beginners, these courses help you learn engineering and machine learning skills essential for an AI engineer career.

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(32,112 ratings)

598,383 already enrolled

Beginner level

Learn More

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Logistic Regression, Artificial Neural Network, Linear Regression, Decision Trees, Recommender Systems, Tensorflow, Advice for Model Development, Xgboost, Tree Ensembles, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Supervised Learning, Anomaly Detection, Unsupervised Learning, Reinforcement Learning, Collaborative Filtering

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IBM DevOps and Software Engineering

Launch your DevOps and Software Engineering Career. Master DevOps, Agile, Scrum, CI/CD and Cloud Native with hands-on job-ready skills.

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Average time: 6 month(s)

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Software Engineering, Python Programming, Application development, Web Application, Flask, Artificial Intelligence (AI), CI/CD, Continuous Integration, Continuous Development, Infrastructure As Code, Automation, Software Testing, Test-Driven Development, Behavior-Driven Development, Test Case, Automated Testing, Kanban, Zenhub, Scrum Methodology, Agile Software Development, Sprint Planning, Software Architecture, Agile and Scrum, Software Development Lifecycle (SDLC), Openshift, Docker, Kubernetes, Containers, Cloud Native, agile, Devops, TDD/BDD, Observability, Telemetry, Monitoring, logging, Tracing, Representational State Transfer (REST), Cloud Applications, Microservices, serverless, Open Web Application Security Project (OWASP), security, Cloud Computing, Hybrid Multicloud, Iaas PaaS Saas, Data Science, Data Analysis, Numpy, Pandas, Distributed Version Control Systems (DVCS), Git (Software), Github, open source, Cloning and forking, Shell Script, Bash (Unix Shell), Extract Transform and Load (ETL), Linux, Linux Commands