PATH 01
Developer → Architect
The T-shaped skill map, the five-rung role ladder, and an honest way to score where your skills actually are today.
Start the path →Learn
Structured learning paths for developers, architects, AI engineers and platform engineers — plus certification guidance and the knowledge base.
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PATH 02
Java/Python Developer → AI Engineer
Seven stages from tokens to production — context engineering, RAG, agents and evals — in the language you already ship in.
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PATH 03
Data Engineer → AI Data Engineer
Embeddings, unstructured pipelines, retrieval as a data product and the semantic layer — on the platform you already run.
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PATH 04
DevOps Engineer → LLMOps/AIOps Platform Engineer
Inference serving, the AI gateway, agent sandboxing, LLM observability and eval gates — the controls you already run.
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PATH 05
Forward Deployed Engineer
Eight breadth areas, a nine-week agentic AI depth stack, and seven client platforms — the T-shape for embedded agentic AI delivery.
Start the path →go deeper
Learning paths archive
Every learning-path article, newest first — Kubernetes and CKA, Spring, Linux, system design and the rest.
Certifications
Which credential is worth the time, split into generic architecture certifications and vendor-specific tracks.
Engineering craft
The skills matrix the paths sit inside — patterns, DevOps, data and ML, quality, delivery, cloud and emerging tech.
Reference Architecture Center
Reference architectures with the diagram, the component breakdown and what to get right — filterable by technology and industry.
Agentic AI knowledge base ↗
The open reference the AI paths link into: frameworks, standards, context engineering, memory, evaluation and production practice.