Modern AI Engineer Roadmap

From first principles to production AI systems.

A complete beginner-to-master architecture for AI engineering: foundations, ML, deep learning, LLMs, AI applications, agents, model infrastructure, optimization, hardware acceleration, security, blockchain, ZKML, and capstone mastery.

Reference Synthesis

• The agent roadmap shaped the agent loop, tools, MCP, memory, multi-agent, evaluation, security, and production sections.
• The AI hardware roadmap shaped inference workload contracts, CUDA and kernel thinking, Jetson and edge deployment, ML compiler concepts, FPGA/HLS, and accelerator co-design.
• The LLM Engineers Handbook shaped the production spine: data pipelines, RAG, domain boundaries, fine-tuning pipelines, evaluation, monitoring, and deployment.
• Hands-On Large Language Models shaped the conceptual LLM order: tokens, embeddings, transformers, prompting, semantic search, RAG, multimodal models, and fine-tuning.
• AI Engineering shaped the product-to-production flow: use-case judgment, model evaluation, prompt systems, RAG and agents, dataset engineering, inference optimization, architecture, guardrails, and feedback loops.
• W3Schools inspired the Exam UI style: simple learning cards, direct practice entry points, visible progress, and immediate answer checking.

Main Path

#	Stage	Simple purpose	Stage artifact
0	Stage 0: Orientation	Build the map before choosing tools.	A learning log, environment checklist, use-case decision memo, and first roadmap plan.
1	Stage 1: Foundations	Become fluent with code, data, math, and systems basics.	A tested Python data application with a CLI or API, setup notes, and a short data report.
2	Stage 2: Machine Learning	Learn how models learn from data and how evaluation can lie.	An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card.
3	Stage 3: Deep Learning	Train, debug, and reason about neural networks.	A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes.
4	Stage 4: Large Language Models	Understand tokens, transformers, generation, adaptation, and evaluation.	An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases.
5	Stage 5: AI Applications	Build reliable AI products around models.	An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis.
6	Stage 6: AI Agents	Build controlled tool-using systems, not vague autonomy.	A tool-using agent with typed tools, memory, traces, task evals, prompt-injection tests, and an architecture README.
7	Stage 7: Model Infrastructure	Operate AI systems as production software.	A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards.
8	Stage 8: Optimization and Hardware Acceleration	Make inference faster, cheaper, and more predictable.	An inference benchmark and optimization report for an open-weight or hosted model workload.
9	Stage 9: AI Security, Blockchain, and ZKML	Secure, govern, and verify AI-enabled systems.	A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo.
10	Stage 10: Mastery	Own a complete AI system end to end.	A capstone AI system with architecture, implementation, evaluation, deployment, observability, cost, security review, and portfolio narrative.

How the Architecture Works

Stages do not all have the same number of parts. Parts do not all have the same number of sub-parts. The structure follows the learning topic. Every sub-part is a folder with an overview, deep dive, and examples/practice page.

Start Here

1. Read How to Use.
2. Open the Roadmap Map.
3. Start Stage 0 unless you can already pass its exit criteria.
4. Use the Exam tab after studying each part.