Definition
AI careers fall into a few main roles. Data analysts explain what happened. Data scientists build predictive models. ML engineers run models in production. GenAI engineers build products on LLMs. Research scientists invent new methods. Each needs a different mix of skills.
In simple words
"Working in AI" covers very different jobs, the way "working in a hospital" covers surgeons, nurses and accountants. Choose by what you enjoy day to day.
Why it matters
Choosing a role early saves you from learning everything. This course's paths are built around these roles, and you can change your mind at any time.
How it works
| Role | Day to day | Core skills |
|---|---|---|
| Data Analyst | Dashboards and answering business questions | SQL, Excel, programming, statistics |
| Data Scientist | Experiments and predictive models | Programming, statistics, ML |
| ML Engineer | Deploying and scaling models | Programming, software engineering, MLOps |
| GenAI Engineer | Building RAG and agent products | Programming, APIs, LLMs, evaluation |
| Research Scientist | Reading and writing papers, new methods | Maths, deep learning, PyTorch |
| Data Engineer | Building data pipelines | SQL, Spark, cloud |
Try it yourself
One product, many jobs
A team builds a crop-advice app. Watch the work pass from person to person.
Career cards: what's the daily work?
Choose a career by what you'd enjoy day to day. Match each role to its daily work.
- Data Analyst
- Data Scientist
- ML Engineer
- GenAI Engineer
- Research Scientist
- Data Engineer
Common mistakes
- Collecting certificates with no projects to show.
- Learning only models and skipping SQL and basic engineering, which most jobs need.
Check yourself
Summary
- AI has several distinct roles.
- Choose by the daily work you enjoy.
- Projects you can show count most.
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