Career Paths into AI
Each path is written against real job-market evidence: what transfers from your current role, what to learn, which projects get interviews and which mistakes to avoid — linked to live role data.
Analyst → AI Automation Engineer
The most accessible AI engineering path for non-engineers: automating real business workflows with AI models and integration tools.
Consultant → AI Solutions Engineer
For consultants and analysts moving toward customer-facing technical AI roles: demos, proofs of concept and solution design.
Data Scientist → Applied AI Engineer
For data scientists who want to own AI systems end to end: production code, serving and the engineering practices research-adjacent roles demand.
Product Manager → AI Product Manager
What changes when the product you manage is probabilistic: evaluation, data strategy, model tradeoffs and the new definitions of quality.
Software Engineer → AI Agent Engineer
How to move from general software engineering into one of the newest AI roles: building agents that plan, call tools and complete multi-step work reliably.
Software Engineer → AI Engineer
A practical transition path for software engineers moving into AI engineering: what transfers, what to learn, what to build and what employers actually ask for.