Artificial Intelligence Engineer
Snapshot
Artificial intelligence engineer apply methods of artificial intelligence in engineering, robotics and computer science to design programs which simulate intelligence including thinking models, cognitive and knowledge-based systems, problem solving, and decision making. They also integrate structured knowledge into computer systems (ontologies, knowledge bases) in order to solve complex problems normally requiring a high level of human expertise or artificial intelligence methods.
Artificial intelligence engineers bridge the gap between theoretical AI concepts and practical applications. Your work involves designing, building, and integrating AI solutions into existing systems. You’ll leverage your expertise in engineering, robotics, and computer science to create programs capable of simulating intelligence, including decision-making and problem-solving. A significant aspect of the role is integrating structured knowledge – like ontologies and knowledge bases – into computer systems to tackle challenges typically requiring expert human knowledge.
- • Design and develop AI models and algorithms for various applications.
- • Integrate knowledge bases and ontologies into computer systems to enhance problem-solving capabilities.
- • Develop and implement cognitive and knowledge-based systems.
Where this occupation is in demand
Reported labour shortages and surpluses, by year. Published for occupation groups, not for individual job titles.
Figures cover Information and communications technology professionals — 75 jobs including this one.
In shortage: Austria, Belgium, Bulgaria, Cyprus and 6 more.
Longest-running shortage: Austria, 3 years.
Select a place on the map to see its figures.
About this source›
Source: ELA/EURES labour shortages and surpluses. Readings are published at occupation-group level, and cover Europe. Editions differ in annex layout and country coverage, so a change between years does not always mean the labour market changed. Countries in grey were not reported, which is not the same as being in balance.
What these words mean
The four things this section reports
- Reported demand
- Whether employers report needing people in this job — a judgement published by a national or EU body, not a count.
- Where it is heading
- Which way employment in this job is expected to move over the coming years, from an official projection.
- Openings
- Roughly how many openings arise — from growth and from people leaving the job.
- Typical pay
- What people in this job typically earn where the source publishes it. Blank does not mean unpaid; it means nobody publishes it for that place.
A measure is left out when nobody publishes it for that place, rather than shown as zero.
Which way the market leans for you
- In your favour
- More openings than people looking — employers are competing for candidates.
- Balanced
- Openings and candidates are roughly matched.
- Competitive
- More people looking than openings — expect to compete.
- Mixed evidence
- Sources disagree, or the same occupation group is short in one part and oversupplied in another.
Every source resolves to one of these four, so there is a single vocabulary to learn. What differs is the evidence behind it, which is printed underneath each verdict — a measured ratio of openings to jobseekers, or an assessment published by a national body.
How this job compares with other jobs in the same country
- Strong
- Among the strongest in that country
- Good
- Stronger than most jobs in that country
- Mixed
- About typical for that country
- Weak
- Weaker than most jobs in that country
This is a rank within one country, not a score you can carry across borders — the registers behind two countries count different people, so the same number means different things in each. It is also why a job can be among the strongest in a country and still show as Competitive: it leads the field in a market that is crowded overall.
Where these come from
Every figure is published by a national statistics office, a public employment service or an EU body, and each card names its source and the period it covers. Some places are counted monthly, others assessed once or twice a year, so two places on the same map can be describing different moments — the date is always shown.
None of this predicts one person’s chances. It describes a market.
Reported demand · Typical pay · Where it is heading (United States, Ireland, Australia, Malta, United Kingdom)
| Reported demand | Typical pay | Where it is heading | |
|---|---|---|---|
| United States | Not reported | $140,300 2025-05 | +19.7% 2024–2034 · BLS 2024-2034 |
| Ireland | Shortage reported ELA/EURES 2025 * | — | +32.3% 2023–2035 · Cedefop 2026 * |
| Australia | Assessed as balanced Jobs and Skills Australia 2025 * | — | — |
| Malta | Shortage reported ELA/EURES 2024 * | — | — |
| United Kingdom | Not reported | £59,593 2025-provisional | — |
* Published for occupation groups, not for individual job titles.
U.S. Bureau of Labor Statistics (OEWS)
Source: Office for National Statistics, Annual Survey of Hours and Earnings, Open Government Licence v3.0
Do you guide students, clients or employees? Use this occupation data in your organisation.
Future Outlook for artificial intelligence engineer
The outlook for artificial intelligence engineer reflects a balanced mix of automation exposure and durable, human-led work.
How are these scores calculated?
The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.
How could artificial intelligence engineer change as AI adoption grows?
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Model-based estimate. Use it as a planning signal, not a guarantee.
Compare this to your profile
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
- define technical requirements
Where AI may become a co-pilot
- analyse big data
- analyse business requirements
- creatively use digital technologies
Tasks most exposed to automation
- use data processing techniques
- create data sets
Vital Signs & AI Vectors
AI Exposure Vectors
0-100%Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks
Exposure to content generation, creative augmentation, and large language model tools
Exposure to workflow automation, decision-support software, and process digitisation
Exposure to physical automation, robotics, and sensor-driven task displacement
Technical Details
NexFuture v3.0 estimates automation exposure natively from ESCO essential-skill groups, weighted by skill mass and calibrated against expert anchors. Scores are probabilistic estimates, not guarantees. See the NexFuture Methodology White Paper for full details.
Measures automation exposure. It does not measure pay, demand, or how many jobs exist near you.
What people in this role usually do
Digital Technology
A typical day as an artificial intelligence engineer
09 09:00 · Morning analyse big data
10 10:30 · Mid-morning analyse business requirements
12 12:00 · Midday create data sets
14 14:00 · Afternoon creatively use digital technologies
15 15:30 · Late afternoon define technical requirements
17 17:00 · Wrap-up apply ICT systems theory
Task order is illustrative. Individual days vary.
What you need to do this work
The skills, knowledge and tools this role calls for — and the traits and rewards that come with it.
-
creatively use digital technologies
Use digital tools and technologies to create knowledge and to innovate processes and products. Engage individually and collectively in cognitive processing to understand and resolve conceptual problems and problem situations in digital environments.
-
use data processing techniques
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
-
design process
Identify the workflow and resource requirements for a particular process, using a variety of tools such as process simulation software, flowcharting and scale models.
-
analyse big data
Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
-
develop creative ideas
Developing new artistic concepts and creative ideas.
-
create data sets
Generate a collection of new or existing related data sets that are made up out of separate elements but can be manipulated as one unit.
-
analyse business requirements
Study clients' needs and expectations for a product or service in order to identify and resolve inconsistencies and possible disagreements of involved stakeholders.
-
develop statistical software
Participate in the various development stages of computer programs for econometric and statistical analysis, such as research, new product development, prototyping, and maintenance.
-
deliver visual presentation of data
Create visual representations of data such as charts or diagrams for easier understanding.
-
apply ICT systems theory
Implement principles of ICT systems theory in order to explain and document system characteristics that can be applied universally to other systems
business process modelling
The tools, methods and notations such as Business Process Model and Notation (BPMN) and Business Process Execution Language (BPEL), used to describe and analyse the characteristics of a business process and model its further development.
data mining
The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
data models
The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.
information architecture
The methods through which information is generated, structured, stored, maintained, linked, exchanged and used.
information categorisation
The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
information extraction
The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
information structure
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
principles of artificial intelligence
The artificial intelligence theories, applied principles, architectures and systems, such as intelligent agents, multi-agent systems, expert systems, rule-based systems, neural networks, ontologies and cognition theories.
Python (computer programming)
The techniques and principles of software development, such as analysis, algorithms, coding, testing and compiling of programming paradigms in Python.
resource description framework query language
The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).
unstructured data
The information that is not arranged in a pre-defined manner or does not have a pre-defined data model and is difficult to understand and find patterns in without using techniques such as data mining.
visual presentation techniques
The visual representation and interaction techniques, such as histograms, scatter plots, surface plots, tree maps and parallel coordinate plots, that can be used to present abstract numerical and non-numerical data, in order to reinforce the human understanding of this information.
Path to become an artificial intelligence engineer
What it typically takes to qualify: education level, where it is a regulated profession, and where to study.
Bachelor's or equivalent level
2,992 programmes in 30 countries teach the core skills of this job. Plus 728 short courses that build single skills.
A few examples, taught in the language of this page:
BSc (Hons) in Computing with Machine Learning / Artificial Intelligence (add-on)
Technological University Dublin · Ireland
Data Science and AI
Radboud Universiteit Nijmegen (RU) Faculteit der NWI · the Netherlands
Diploma in Artificial Intelligence and Working into the Future
CCT College Dublin · Ireland
Postgraduate Diploma in Science in AI for Business
National College of Ireland · Ireland
Artificial Intelligence & Engineering Systems
Technische Universiteit Eindhoven · the Netherlands
Applied Machine Learning
Technological University Dublin · Ireland
These are 6 of 3,720.
Sources
- 1. Source: Europass Qualification Dataset Register (European Commission)
- 2. Bron: DUO open onderwijsdata
Growth Pathways & Similar Roles
Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.
Where does artificial intelligence engineer fit?
Similarity scores based on skill overlap from ESCO data.
For organisations
The data on this page, working for your students, clients and staff.
Frequently asked questions
What kind of problems do artificial intelligence engineers typically solve?
Is a background in robotics essential to become an artificial intelligence engineer?
What work styles and values are important for success in this role?
How much does Artificial Intelligence Engineer pay in the United States?
Sources: ESCO O*NET ELA/EURES Cedefop BLS Data updated October 3, 2026 About our data