AI Engineering Learning Paths
Role-based roadmaps with curated guides, projects, and milestones. Pick your target role and follow the path.
5 career paths·6–12 month timelines·Beginner to advanced
01
Learning Path6–12 months$130k–$220kDemand: Very High
AI Engineer Path: From Developer to Hired in 16 Weeks (2026)
Random tutorials will not get you hired. This structured 16-week curriculum covers LLMs, RAG, fine-tuning, agents.
02
Learning Path6–10 months$130k–$210kDemand: High
AI Product Engineer: Build AI Features Users Love (2026)
AI features that users ignore are wasted effort. Learn product-layer AI integration, RAG for features, UX patterns.
03
Learning Path12–24 months$160k–$350kDemand: Moderate
AI Research Engineer: Bridge Papers and Production Code (2026)
Reading papers is easy. Implementing them is not. Learn paper implementation, experiment tracking, model evaluation.
04
Learning Path9–15 months$150k–$260kDemand: Very High
LLM Engineer Path: From Prompts to Production at Scale (2026)
Prompt engineering is step one. Production serving is the goal. The full path — RAG, fine-tuning with LoRA, evaluation.
05
Learning Path8–14 months$140k–$230kDemand: High
ML Engineer Path: From Sklearn to Production Models (2026)
Sklearn tutorials do not prepare you for production. This path does — supervised learning, feature engineering, deployment, monitoring.