Free guide
Eight AI careers, and how to move into each.
What each role does day to day, the skills hiring teams screen for, the jobs people usually move in from, and one project that shows you can do the work. Free.
- Engineering
AI engineer
Builds applications and agents on top of AI models, with the tests and controls that make them reliable.
The full AI engineer pathThe work
- Connect models to tools, data, and systems
- Design agents with approval steps
- Write evaluations and monitor quality
Skills to show
- A programming language, usually Python or TypeScript
- APIs, retrieval, and tool use
- Evaluation sets and testing
Common routes in
- Software engineer
- Data engineer
- Technical founder
- Engineering
Machine learning engineer
Trains, adapts, and serves models, and measures whether they are good enough to ship.
The full Machine learning engineer pathThe work
- Prepare training and evaluation data
- Fine-tune and compare models
- Serve models and track cost and latency
Skills to show
- Python and an ML framework
- Statistics and evaluation
- Data pipelines and deployment
Common routes in
- Data scientist
- Software engineer
- Research graduate
- Product
AI product manager
Decides what an AI feature should do, how its quality is judged, and when it is ready for customers.
The full AI product manager pathThe work
- Write specifications and acceptance criteria
- Weigh quality, cost, and risk
- Plan staged launches with quality gates
Skills to show
- Product practice
- Evaluation basics
- Clear writing about risk and trade-offs
Common routes in
- Product manager
- Business analyst
- Engineering manager
- Operations
AI operations specialist
Runs AI systems day to day: watches the queues, resolves exceptions, corrects outputs, and reports.
The full AI operations specialist pathThe work
- Monitor agents and their queues
- Handle exceptions within policy
- Turn corrections into improvements
Skills to show
- Process discipline
- Comfort with AI tools and logs
- Judgment about when to escalate
Common routes in
- Operations lead
- Customer support
- Back-office specialist
- Quality
AI evaluation and quality specialist
Designs the tests that tell a team whether an AI system is doing the job, and finds where it is not.
The full AI evaluation and quality specialist pathThe work
- Build and maintain evaluation sets
- Review outputs against written criteria
- Report quality trends and failure cases
Skills to show
- Test design
- Careful written judgment
- Basic scripting or spreadsheet analysis
Common routes in
- QA engineer
- Support specialist
- Domain expert
- Risk
AI governance and risk analyst
Assesses how AI is used, writes the policies and controls, and checks that they hold.
The full AI governance and risk analyst pathThe work
- Review AI use cases and vendors
- Write policies and approval boundaries
- Audit data flows and controls
Skills to show
- Risk and compliance practice
- Data protection basics
- Clear policy writing
Common routes in
- Compliance analyst
- Security analyst
- Legal operations
- Analytics
AI-enabled analyst
Uses AI to automate analysis and reporting, and checks the results before anyone relies on them.
The full AI-enabled analyst pathThe work
- Automate recurring analysis
- Build AI-assisted reporting
- Validate results against the source data
Skills to show
- SQL and spreadsheets
- A scripting language
- Skepticism about numbers
Common routes in
- Data analyst
- Financial analyst
- Operations analyst
- Customer-facing
AI solutions specialist
Helps customers or internal teams choose, set up, and adopt AI tools for their work.
The full AI solutions specialist pathThe work
- Map a team’s work to AI tools
- Configure and pilot tools
- Train users and gather feedback
Skills to show
- Consulting or customer success
- Hands-on AI tool use
- Workshop facilitation
Common routes in
- Solutions engineer
- Customer success manager
- Trainer
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