00
Engineering Foundations
Python, Git, HTTP, JSON, async, testing and SQL.
Free public learning roadmap
Modern AI Engineering Path 2026
A production-first roadmap for moving from using AI to engineering AI systems that can be evaluated, secured, observed, debugged and deployed.
Created by Mnawar Mohammed · Publicly accessible on GitHub
Why this exists
The LLM is not the product
Calling a model API is easy. Building a system that survives real users, real data, real costs and real failure modes is the engineering challenge.
This roadmap connects the pieces around the model: structured outputs, retrieval, evaluation, tools, agents, security, APIs, observability and deployment.
The roadmap
12 modules from foundations to production
Follow the full sequence for ML + AI Engineering, or use the product track to move into modern AI systems faster.
01
ML & Neural Foundations
Core ML, neural networks, embeddings and transformers.
02
LLM APIs
Model APIs, context, streaming, retries, cost and multimodal inputs.
03
Structured Outputs
Schemas, validation, typed responses and tool contracts.
04
Production RAG
Embeddings, chunking, retrieval, reranking and citations.
05
Evals
Eval datasets, regression control and quality measurement.
06
Agents & Workflows
Tool use, graphs, bounded loops and human approval.
07
MCP
Reusable tools, resources and external system integration.
08
Safety & Security
Prompt injection, least privilege and authorization boundaries.
09
Production Services
FastAPI, auth, queues, persistence and service architecture.
10
Observability & Reliability
Tracing, latency, fallbacks, caching and cost control.
11
Deployment & Capstone
Docker, CI/CD, staging, rollback and a complete project.
A decision framework
Complexity should be earned
Start with the simplest architecture that can solve the problem. Add complexity only when the problem requires it.
Deterministic code
↓
One model call
↓
Structured output
↓
Retrieval
↓
Tools
↓
Workflow
↓
Agent
↓
One model call
↓
Structured output
↓
Retrieval
↓
Tools
↓
Workflow
↓
Agent
Core principles
Evals before optimization
Schemas over prose
Retrieval before fine-tuning for changing knowledge
Workflows before agents
Human approval before irreversible actions
Bound every loop
Trace everything important
Explore the complete roadmap
The full curriculum, module guides, code examples, eval runner, FastAPI example, Docker setup and CI workflow are available on GitHub.
Open on GitHubOpen Modern AI Engineering Path 2026If you find it useful, consider starring the repository so more learners can discover it.
About the creator
Created by Mnawar Mohammed
Mnawar Mohammed is a writer, entrepreneur and AI architect focused on practical AI systems, digital products and turning complex ideas into useful frameworks.
Explore Mnawar's work