Browse Definitions :

How to become an artificial intelligence engineer

Learn what it takes to launch a rewarding career as an AI engineer, including required skills, responsibilities, qualifications, educational opportunities and top salaries.

Artificial intelligence engineers are in great demand and typically earn six-figure salaries. An individual who is technically inclined and has a background in software programming may want to learn how to become an artificial intelligence engineer and launch a lucrative career in AI engineering.

What is artificial intelligence?

AI is instrumental in creating smart machines that simulate human intelligence, learn from experience and adjust to new inputs. It has the potential to simplify and enhance business tasks commonly done by humans, including business process management, speech recognition and image processing.  

The majority of AI applications today -- ranging from self-driving cars to computers that play chess -- depend heavily on natural language processing and deep learning. These technologies can train computers to do certain tasks by processing massive amounts of data and identifying patterns in the data.

In addition to analyzing information faster, AI can spur more creative thinking about how to use data by providing answers that humans may not have considered.

What are AI engineers and what do they do?

AI engineers develop, program and train the complex networks of algorithms that encompass AI so those algorithms can work like a human brain. AI engineers must be experts in software development, data science, data engineering and programming. They uncover and pull data from a variety of sources; create, develop and test machine learning models; and build and implement AI applications using embedded code or application program interface (API) calls.

AI's components

What are the responsibilities of AI engineers?

Artificial intelligence engineers are mainly responsible for building AI models using machine learning algorithms and deep learning neural networks to extract business insights. An AI engineer's responsibilities include the following:

  • Build AI models from the ground up and help stakeholders in the organization, including business users and product managers, understand what outcomes the models will yield.
  • Perform statistical analysis and interpret the results to help the company make better business decisions.
  • Create and manage AI product development and infrastructure.
  • Create data transformation and data ingestion infrastructures.
  • Automate the infrastructure used by the data science team.
  • Develop machine learning applications as per requirements.
  • Run AI and machine learning experiments and tests.
  • Train and retrain systems when needed.
  • Transform machine learning models into APIs so other applications can interact with them.
  • Coordinate tasks with other members of the AI team.
  • Collaborate with the electronics and robotics departments.

What are the required skills and education for AI engineers?

AI engineers need to have a combination of technical and nontechnical business skills.

Technical skills

  • Extensive knowledge of statistics, calculus or algebra to work with algorithms and an understanding of probability to interact with some of AI's most common machine learning models, including Naive Bayes, Hidden Markov and Gaussian mixture models.
  • Adept with popular programming languages such as Python, C++, Java and R to develop and deploy AI models.
  • Solid understanding of algorithms and applied mathematics to build, modify and use AI models.
  • Good knowledge of natural language processing, which combines computer science, information engineering, linguistics and AI in one system, plus the ability to program the system to process and analyze large data sets.

Nontechnical business skills

  • Ability to clearly communicate project goals, timelines and expectations with stakeholders, including data scientists, data analysts, research analysts, software engineers, marketing managers and product teams.
  • Ability to think critically, creatively and analytically to solve problems in real time, evaluate numbers, trends and data and develop conclusions based on findings, question established business practices and suggest new approaches to the AI process.
  • Ability to work in a collaborative and supportive work environment.
  • Possess business acumen and industry knowledge.

Education

  • Bachelor's degree in a field related to AI, such as data science, computer science, IT or statistics.
  • Master's degree (though not typically required) in such disciplines as data science, mathematics, cognitive science or computer science.
  • Enrollment in additional AI-related courses and certification programs.

What is the salary of an AI engineer?

The average annual salary for an AI engineer in the U.S. was $164,769 as of July 2021, according to ZipRecruiter. Annual AI engineer salaries in the U.S. can be as low as $90,000 and as high as $304,500, while most AI engineer salaries currently range from $142,500 to $173,000, with top earners in the U.S. earning $216,500 annually.

What careers are available to AI engineers?

IT professionals who pursue careers as artificial intelligence engineers can provide organizations with valuable insights into future issues and critical business decisions. Many industries use AI technology in a variety of applications, including the following:

What courses and certifications are available to AI engineers?

  • Artificial Intelligence Graduate Program by Stanford University School of Engineering
  • AI for Everyone by Andrew Ng (Coursera)
  • IBM Applied AI Professional Certificate (Coursera)
  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning (Coursera)
  • Artificial Intelligence A-Z: Learn How to Build an AI (Udemy)
  • Artificial Intelligence Course: Reinforcement Learning in Python (Udemy)
  • Master the Fundamentals of AI and Machine Learning (LinkedIn Learning)

Next Steps

9 top AI and machine learning trends for 2021

4 main types of artificial intelligence: Explained

4 explainable AI techniques for machine learning models

10 steps to achieve AI implementation in your business

Top AI conferences and virtual events of 2021

Dig Deeper on Artificial intelligence - machine learning

SearchCompliance
  • ISO 31000 Risk Management

    The ISO 31000 Risk Management framework is an international standard that provides businesses with guidelines and principles for ...

  • pure risk

    Pure risk refers to risks that are beyond human control and result in a loss or no loss with no possibility of financial gain.

  • risk reporting

    Risk reporting is a method of identifying risks tied to or potentially impacting an organization's business processes.

SearchSecurity
  • Twofish

    Twofish is a symmetric-key block cipher with a block size of 128 bits and variable-length key of size 128, 192 or 256 bits.

  • walled garden

    On the internet, a walled garden is an environment that controls the user's access to network-based content and services.

  • potentially unwanted program (PUP)

    A potentially unwanted program (PUP) is a program that may be unwanted, despite the possibility that users consented to download ...

SearchHealthIT
SearchDisasterRecovery
  • What is risk mitigation?

    Risk mitigation is a strategy to prepare for and lessen the effects of threats faced by a business.

  • fault-tolerant

    Fault-tolerant technology is a capability of a computer system, electronic system or network to deliver uninterrupted service, ...

  • synchronous replication

    Synchronous replication is the process of copying data over a storage area network, local area network or wide area network so ...

SearchStorage
  • Remote Direct Memory Access (RDMA)

    Remote Direct Memory Access (RDMA) is a technology that enables two networked computers to exchange data in main memory without ...

  • storage (computer storage)

    Data storage is the collective methods and technologies that capture and retain digital information on electromagnetic, optical ...

  • storage medium (storage media)

    In computers, a storage medium is a physical device that receives and retains electronic data for applications and users and ...

Close