Career Spotlight: AI Engineer [VIDEO]

Written by Coursera Staff • Updated on

Imagine shaping the future of technology. That's the power of an AI Engineer - the brilliant minds behind self-driving cars, personalized recommendations, and so much more!

[Thumbnail] Career Spotlight: AI Engineer

Imagine being at the forefront of technological innovation — that's the world of an AI engineer. These modern-day wizards develop the tools and systems that bring artificial intelligence to life in the real world.

As an AI engineer, you’ll build intelligent applications using machine learning techniques like training algorithms with data to mimic human reasoning and decision-making. From personalizing streaming services to powering self-driving cars, the possibilities are endless.

With AI projected to contribute over $15 trillion to the global economy by 2030, the demand for AI talent is skyrocketing. This fast-growing field offers an average salary over $115,000 in the US alone.  

So if you’re a problem solver with a passion for coding and analytics, a career as an AI engineer could be your calling. Gain skills in programming, data frameworks, and algorithms through online courses, and get ready to shape the future of intelligent technology.

IBM

professional certificate

IBM AI Engineering

Get job-ready as an AI engineer . Build the AI engineering skills and practical experience you need to catch the eye of an employer in less than 4 months. Power up your resume!

4.5

(7,569 ratings)

145,101 already enrolled

Intermediate level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Regression Analysis, Data Manipulation, Tensorflow, Generative AI Agents, Machine Learning, Unsupervised Learning, Reinforcement Learning, Applied Machine Learning, Generative AI, Deep Learning, PyTorch (Machine Learning Library), Scikit Learn (Machine Learning Library), Supervised Learning, Image Analysis, Prompt Engineering, Python Programming, Data Science, Large Language Modeling, Keras (Neural Network Library), Natural Language Processing, User Interface (UI), Databases, Data Processing, Artificial Neural Networks, Computer Vision, Predictive Modeling, Scientific Visualization, Verification And Validation, Network Model, Network Architecture, Machine Learning Methods, Dimensionality Reduction, Feature Engineering, Statistical Analysis, Classification And Regression Tree (CART), Machine Learning Algorithms, Text Mining, Semantic Web, Statistical Methods, Data Ethics, Probability & Statistics, Artificial Intelligence, Artificial Intelligence and Machine Learning (AI/ML), Time Series Analysis and Forecasting, Performance Tuning, ChatGPT, OpenAI, Data Pipelines, Training and Development, User Feedback, Quality Assessment

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Advance in your career with recognized credentials across levels.

Unlock 10,000+ expert-led courses today. Now $159 off.