Artificial Intelligence (AI) Engineer Job Description, Skills, and Salary
Get to know about the duties, responsibilities, qualifications, and skills requirements of an AI engineer. You can use our job description template in this article to produce your own. We also provide you with information about the salary you can earn as an AI engineer.
Who is an Artificial Intelligence Engineer?
Artificial Intelligence engineering focuses on the development of tools, systems, and processes that enable the use of AI in the real world. Artificial Intelligence can be defined as any application where machines mimic human activities, such as solving problems and learning. With the use of AI, algorithms are ‘trained’ using data to learn and produce better results.
An AI engineer is a professional who develops programs and trains the complex networks of algorithms that make up AI to act like human beings. The position requires experience in software development, programming, data science, and computer science.
Although the job involves the application of data science, companies rarely ask AI engineers to write code to develop an application. Most AI engineers search and retrieve data from a variety of sources to create, develop and test machine learning models. They then build and deploy AI applications through API calls or embedded code.
To be effective in their role, AI engineers must understand AI and its use in organizations. They must also be able to effectively manage the complexity of machine learning models so that they can be used efficiently in different contexts to evaluate their effectiveness by measuring their performance against specific goals.
Artificial Intelligence (AI) Engineer Job Description
Below are the AI Engineer job description examples you can use to develop your resume or write a job description for your employee. Employers can also use it to sieve out job seekers when choosing candidates for interviews.
The duties and responsibilities of an AI Engineer include the following:
- Researching new ways to improve the performance of machine learning models.
- Working with other teams in the company to develop and implement new AI-based systems.
- Identifying and solving problems with existing AI models.
- Controlling the complexity of coded applications so they can be effectively applied in a variety of settings.
- Evaluating the effectiveness of machine learning models by measuring their performance against specific goals or criteria.
- Participating in research projects related to AI and its application within organizations.
- Conducting audits of data processing and machine learning systems to ensure accuracy and compliance with company policies.
- Providing technical support to other team members
- Developing training materials for new or inexperienced users of AI-based applications
- Researching new methods and technologies that could be used in AI engineering
- Updating the documentation for all AI-based systems within the company.
- Writing code to implement AI-based solutions, whether it be in a specific software package or a custom application.
- Working with other teams within the company to identify and solve problems with customer data.
- Ensuring that all AI-based systems are effectively monitored and maintained.
- Creating and managing the AI development process and overall product infrastructure.
- Performing statistical analysis and interpreting the results to support decision-making within the company.
- Automating key infrastructure for the data science team
- Creating artificial intelligence models that make predictions based on big data.
- Explaining the usefulness of the artificial intelligence models created to different people in the company, such as employees and product managers.
- Turning machine learning models into APIs to interact with other applications.
- Turning machine learning models into APIs so that other applications can access them.
- Developing minimum viable products based on machine learning.
- Using AI to bring new capabilities to the business.
- Building AI models from scratch and helping different parts of the organization (e.g. product managers and stakeholders) understand the results from the model.
- Setting up an infrastructure for ingesting and transforming data.
- Conducting statistical analysis and adjusting the results so that the organization can make more informed decisions.
- Applying logic, probability analysis, and machine learning concepts to problem-solving.
- Analyzing systems for effective monitoring and management of development projects.
- Applying AI best practices in speech recognition, data processing, data mining, and robot control.
Other duties and responsibilities include:
Software design
AI engineers need to master machine learning, modeling, and validation. They must evaluate and decide whether an AI system can achieve the purpose for which it was designed.
Creating and using AI algorithms
Intelligent AI algorithms use iterative processing to enable software automation. The AI engineer is responsible for writing the basic code needed to make the machine work.
Building infrastructure for data science
AI is primarily used to explore and analyze data. The role of the AI engineer is therefore to build and manage AI infrastructure.
Data analysis
An AI engineer collects data and analyses it using machine learning algorithms to identify gaps. The AI engineer is also responsible for ensuring that AI tools are updated as they evolve and continue to provide operational benefits to users.
Natural language processing
With the emergence of intelligent automation and voice control, natural language processing is an important task for the AI engineer. Voice-based assistants such as Siri or Alexa, self-driving cars, etc. depend on an accurate and fast understanding of human speech, and AI engineers make this possible.
Image processing
Artificial intelligence is used in global surveillance and monitoring. These systems can observe the environment and react to any scenario depending on the parameters defined by the AI engineers building them. Image processing is therefore one of the tasks of the AI engineer.
Qualifications
Jobs in artificial intelligence require strong programming and data science skills. Employers are therefore often looking for candidates with a combination of formal education and practical experience.
To start a career in artificial intelligence, a person may need to meet certain training, certification, and experience requirements:
- A bachelor’s degree in data science, computer science, programming, or another technical field.
- A technical knowledge base that includes multiple programming languages.
- Experience with machine learning, and other artificial intelligence systems and training techniques.
For a more advanced position in this field, future artificial intelligence developers should consider a master’s degree in data science, data analytics, machine learning, artificial intelligence, or another field that combines technology with algorithms and logic. With an advanced degree, artificial intelligence professionals can use their education and credentials to move into leadership roles.
Despite these requirements, artificial intelligence technology is a broad term that encompasses professionals with different skill sets. It is a field that specializes early in a career and expands over time.
Essential Skills
Artificial intelligence engineers use a range of skills to help them succeed in their careers. By developing these skills, you can become a better AI engineer.
The essential skills for that are important for AI engineers to possess include the following:
Understanding of statistics, probability, and linear algebra.
AI engineers create models using algorithms based on statistics, algebra, and calculus. Advanced mathematical knowledge helps AI engineers build more complex models. It is also useful to be familiar with probability theory to be able to work with some of the most common machine learning models in AI, such as Hidden Markov, Gaussian Mixture, and Naive Bayes models.
Programming skills
It is useful for AI engineers to have a good knowledge of popular programming languages, such as C++, Java, R, and Python. These programming languages help artificial intelligence engineers to develop and implement artificial intelligence models. Knowing more than one programming language also helps them to work on a wider range of projects and makes it easier to convert a project from one language to another.
Analytical skills
Projects often require artificial intelligence engineers to analyze and interpret important data. For example, they may need to evaluate the results of an AI application to ensure that the results are reasonable.
Business knowledge and insight
Successful AI projects can help in solving important problems for your organization. With business acumen, you can turn technical ideas into effective business projects. Regardless of the sector, you work in, it is good to have a basic understanding of how the business works, its target market, and its competitors in the market.
Communication skills
Artificial intelligence engineers often need to communicate data and technical information to different people with different technical expertise. For example, an entrepreneur may require all departments in an organization to introduce and demonstrate a new AI model.
Therefore, it is advantageous to have good oral and written communication skills. This includes the ability to address complex AI issues and explain the fundamentals to people without advanced technical knowledge.
Collaboration skills
People in this field often work in teams with other AI developers and IT professionals, so being able to work effectively and efficiently in teams is useful. For example, a team may work simultaneously on an AI project, or individual AI engineers may be responsible for separate parts of the project; AI engineers can work in such a group environment and ensure consistency between their part of the project and other parts.
Critical thinking
Critical thinking is useful for creating innovative AI models and solutions. This means finding different possible solutions to the same problem. Critical thinking helps to analyze problems from different angles and propose different solutions.
How to Become an AI Engineer
Here are some typical pathways to becoming an AI engineer.
1. Acquire the right skills
Most entry-level AI engineer positions require at least a relevant university degree. This could be, for example, a degree in computer engineering, information science, or computer information systems. This may reflect AI technical skills acquired during university studies or through self-study.
Boot camps are also a good way to learn some important skills in less than a year; programs for AI engineers teach important concepts such as data science and programming. If you are self-taught, it is also useful to focus on learning as many programming languages as possible.
2. Create a portfolio of your work
The best way to show off your skills to potential employers is to create a portfolio of your work. This could be work you did during your university days or projects you worked on in your spare time. This shows employers what you can do as an AI engineer and allows you to talk about interesting projects from your past.
Therefore, to show all your skills, try to include projects that require multiple skills, such as translation and analysis. If you struggle to find suitable projects for your portfolio, consider working on new projects to gain experience.
3. Find an internship
Internships are a great way to experience the real environment of an AI engineer and show potential employers that you can work in this field. Internships provide training, and professional networking and help prepare you for a career. In some cases, an internship can lead to a permanent job.
Many internship opportunities can match your career ambitions. Smart technology and cybersecurity are fast-growing fields that offer many opportunities for AI engineers. Try to find an internship that matches your skills and long-term career goals, for example in deep learning, programming, or other areas.
4. Think about your career path
Artificial intelligence is now embedded in a multitude of applications and sectors, giving AI engineers a great deal of flexibility in their choice of job. Think carefully about the sector you want to work in – manufacturing, healthcare, construction, etc.
5. Get certified as an artificial intelligence engineer
Specific AI certifications are a good way to show employers that you are qualified in a particular area of Artificial Intelligence. It is not essential for all AI engineering jobs, but it increases your chances of getting a job in your chosen field. Several certification programs allow you to show employers that you have experience and expertise in the field and that you are an excellent independent worker.
In some cases, there are minimum requirements for admission to certification programs, such as a university degree or work experience in the field. Some programs require you to pass a series of exams before you can be certified.
6. Create a CV
An Artificial Intelligence engineer CV is a great way to showcase your skills, experience, and expertise. Make sure your CV reflects what the employer expects from you, including the skills and competencies specific to AI engineers.
Show your qualifications and experience in different programming languages; AI engineers are recruited by different sectors, so check and revise your CV if necessary.
Where to work as an AI Engineer
Most AI engineers work for large technology companies such as Google, Microsoft, and Apple, but a growing number of smaller companies also employ AI engineers, who often work in offices alongside other computer scientists and engineers. They may also work in research centers, laboratories, and manufacturing companies.
AI Engineer Salary Scale
In the United States, the salaries of AI engineers range from $108,000 to $162,000, with an average salary of $135,000.
