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Jobs and Careers in AI: 18 Roles to Know
Interested in working in AI? There are roles for people who enjoy coding, maths, writing, design, business and checking details.
Here is what each role involves. Job titles can overlap, and the duties and entry requirements differ between employers.
1. AI Research Scientist
Studies new ways to make AI learn, reason and solve problems. This role often involves experiments, advanced maths and publishing research.
2. Machine Learning Engineer
Builds and trains systems that learn from data, such as a tool that spots suspicious payments. Tests how well they work before putting them into use.
3. AI Application Developer
Builds apps that use AI models, such as a study assistant or customer-support tool. Connects the AI to the app and checks its responses.
4. Data Scientist
Studies data to answer questions and build prediction models. For example, they might help a shop estimate which products customers will buy.
5. Data Engineer
Builds systems that collect, clean and organise data. Makes sure the data needed by AI teams is available and reliable.
6. Data Annotator
Labels pictures, text or audio so AI systems have useful examples to learn from. For example, they might mark road signs in images or identify the language in a recording.
7. AI Trainer or Response Evaluator
Reviews AI answers and provides feedback on accuracy, clarity and usefulness. Some roles need specialist knowledge in subjects such as science, writing or coding.
8. Prompt Engineer
Designs and tests instructions that help an AI system produce useful, consistent results. This work is also often part of a developer’s or other specialist’s job.
9. AI Automation Specialist
Connects AI tools with business processes to reduce repetitive work. For example, they might build a workflow that sorts enquiries and prepares draft replies for someone to check.
10. MLOps Engineer
Helps move machine-learning models into everyday use and keeps them running. Monitors performance and manages updates when data or models change.
11. Computer Vision Engineer
Builds AI that understands pictures and video. Their work can help inspect products, recognise objects or process scanned documents.
12. Natural Language Processing Engineer
Builds systems that work with human language. Examples include translation, text search and tools that identify the meaning of a customer’s message.
13. Speech AI Engineer
Works on systems that recognise spoken words or generate speech. Helps build features such as voice assistants, captions and transcription.
14. Robotics Engineer
Builds and programs machines that can sense their surroundings and carry out physical tasks. Some use AI to help robots recognise objects or plan movements.
15. AI Product Manager
Decides which problems an AI product should solve and helps the team choose priorities. Works with developers, designers and users to check whether the product is useful.
16. AI UX Designer
Designs how people interact with AI tools so they are easy to understand and use. Makes it clear when users should review an answer or approve an action.
17. AI Governance Specialist
Helps an organisation set rules for responsible AI use. Reviews issues such as privacy, fairness, documentation and accountability.
18. AI Security Specialist
Tests and protects AI systems against attacks, misuse and data leaks. Checks how the system handles malicious instructions and access to sensitive information.
Choose one role that interests you, read a few real job descriptions and build a small project that shows the relevant skill. These are career examples, not a list of current vacancies.
You can explore real roles on IBM’s AI careers page and its data and analytics careers page.
AI has been around much longer than ChatGPT. In 1956, researchers met at Dartmouth College to explore whether machines could do things we associate with human intelligence. Early AI often followed rules written by people. Later, machine learning helped computers find patterns in examples. Today’s AI can recognise speech and images, answer questions and create new content.
There are three broad forms when we talk about how capable AI is:
• Narrow AI does particular tasks, such as recommending a video, recognising a face or helping you write. The AI tools we use today fall in this broad category, even when they can do several tasks.
• Artificial general intelligence (AGI) would learn and handle a wide range of tasks at a human-like level. Researchers are still debating how to define and achieve it.
• Artificial superintelligence (ASI) would be more capable than humans across many areas. This remains hypothetical.You may also hear names based on what AI does: generative AI creates text, images or audio; predictive AI estimates what may happen; and computer vision helps machines understand pictures. These are uses of AI, not separate stages of intelligence.
Want to read more? See Dartmouth’s history of AI: https://ai.dartmouth.edu/our-story and IBM’s explanation of AI types: https://www.ibm.com/think/topics/artificial-intelligence-types
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