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AI Roadmap
AI Engineer Roadmap
AI engineering is its own discipline now not ML research, not vanilla web dev. This roadmap takes you from Python fundamentals to deploying production-grade RAG, agents, and evaluation systems.
8 stages 4-9 months All levels Software engineers transitioning into AI, or new builders starting from scratch
By the end you’ll be able to
- 1Build production LLM features end-to-end
- 2Implement RAG with retrieval, re-ranking, and evals
- 3Ship multi-step AI agents with tool use
- 4Run evals that catch regressions before users do
- 5Choose the right model and infrastructure for the task
01
Stage 1 3-6 weeks
Python & TypeScript Fundamentals
The two languages of AI engineering
Most AI work happens in Python (training, ML libraries, agents) and TypeScript (product surfaces). You don't need to be a master but you need to read, write, and debug both fluently. Async, types, packaging, and standard library are non-negotiable.
Skills to develop
Python 3.11+ syntax, async, typingTypeScript with Zod / Valibotuv or poetry for Python depspnpm or bun for JS depsReading tracebacks and stack tracesGit, branches, PRs
02
Stage 2 2-4 weeks
Math & ML Intuition
Just enough to read papers and reason about models
You don't need a PhD to be an AI engineer in 2026, but you need intuition for vectors, probability, and how a transformer learns. The goal is to read the Anthropic, OpenAI, and DeepMind blog posts without losing the plot.
Skills to develop
Linear algebra (vectors, dot products)Probability basicsGradient descent intuitionTokenization and embeddingsWhat attention actually doesLoss functions in plain English
03
Stage 3 4-8 weeks
LLM Fundamentals
Prompts, structured output, tools, and the Anthropic / OpenAI / Vercel SDKs
This is where most AI engineering work actually lives. Get fluent with the major SDKs, learn to design prompts that produce structured output, and understand tool use. By the end you should be able to wire a real LLM call into a real product in under an hour.
Skills to develop
Anthropic & OpenAI Python / JS SDKsPrompt engineering & few-shot patternsStructured output via JSON schemaTool use / function callingStreaming responsesCost & latency tradeoffs
Resources
- DocsAnthropic Prompt Engineering GuideFREE
- DocsOpenAI CookbookFREE
- DocsVercel AI SDK DocsFREE
- BookBuilding LLMs for Production by Bouchard, Peters
04
Stage 4 4-6 weeks
Retrieval-Augmented Generation (RAG)
Embeddings, vector DBs, hybrid search, re-ranking
Most production AI features need to ground answers in your own data. Build at least one real RAG pipeline end-to-end: chunk documents, embed them, store in a vector DB, retrieve, and generate. Don't stop at 'it works' measure quality.
Skills to develop
Document chunking strategiesEmbedding models (OpenAI, Voyage, Cohere)Vector DBs (pgvector, Pinecone, Weaviate, Qdrant)Hybrid search (BM25 + vector)Re-rankingCitations and source tracking
Resources
- DocsPinecone RAG HandbookFREE
- DocsLlamaIndex DocsFREE
- ReadRAG Glossary entryFREE
- ProjectBuild a RAG app on your own docsFREE
05
Stage 5 4-8 weeks
Agents & Tool Use
From single calls to multi-step autonomous workflows
Once you have prompting, structured output, and RAG, the next step is agents systems where the model plans, calls tools, observes results, and iterates. Build an agent that does something useful in a sandbox, then add guardrails before letting it touch anything important.
Skills to develop
Agent loop patternsTool / function calling designMulti-agent orchestrationMemory (short-term & long-term)Sandboxing dangerous toolsHuman-in-the-loop approval
Recommended tools
06
Stage 6 2-4 weeks
Evals & Observability
How you actually know your AI is working
This is the difference between a hobby project and a production system. Build an eval set, automate it in CI, and trace every LLM call in production. If you only learn one thing past the basics, learn this it's what separates AI engineers from AI tinkerers.
Skills to develop
Designing eval datasetsAutomated grading (LLM-as-judge, exact match, rubric)Regression detectionCost & latency monitoringTracing and replay
07
Stage 7 1-2 weeks
Protocols & Frameworks
MCP, AG-UI, A2A the connective tissue
The agentic ecosystem stabilized fast. MCP for tools, AG-UI for chat surfaces, A2A for agent-to-agent communication. You don't need to use all of them, but you need to know what each is for so you don't reinvent it.
Skills to develop
Model Context Protocol (MCP) serversAG-UI for streaming agent surfacesA2A for inter-agent commsWhen to pick a protocol vs a framework
08
Stage 8 4-8 weeks
Ship a Real Product
End-to-end project to put on your CV
All theory collapses on contact with users. Pick one real problem, build an end-to-end AI product (UI, API, RAG, evals, observability, deployment), and put it in front of 10 strangers. The feedback will teach you more than any course.
Skills to develop
Full-stack integrationDeployment to Vercel / Cloudflare / FlyUser feedback loopsIteration based on evals + feedback
Recommended tools
Resources
- ProjectBuild in Public on X / LinkedInFREE
- ToolAI Tools DirectoryFREE
Explore other roadmaps
Ready to build with what you've learned?
We pair these roadmaps with hands-on engagements pair-programming, code review, and architecture support.