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A hands-on, role-based curriculum for engineers who build with AI — LLM apps, RAG, agents, and multi-agent systems, from first app to systems architect.
🌐 Website: ai-engineering-curriculum.github.io · part of the AI Career Curriculum · 📊 Completeness matrix
This org covers AI engineering — building with the models: LLM application development, retrieval and RAG, agents and multi-agent systems, evaluation, guardrails, harness engineering, and production operations. Every role track has a paired learning repository (modules, lecture chapters, exercises, quizzes) and a solutions repository with reference walkthroughs.
At a glance:
- 🏢 22 curriculum repositories (11 learning + 11 solutions)
- 📚 11 role tracks
- 🤖 Agentic track of four rungs: Agentic AI Developer → Engineer → Senior → Systems Architect
One of 6 sibling orgs in the AI Career Curriculum ecosystem, organized by what you do relative to a model:
- AI Infrastructure Curriculum — run the platforms (Kubernetes, GPUs, training infra, serving, MLOps, IaC, SRE). (site)
- ML Engineering Curriculum — build & train the models (data, fine-tuning, pretraining, RLHF, evals). (site)
- AI Engineering Curriculum (you are here) — build with the models (agentic apps, RAG, multi-agent systems).
- AI Governance Curriculum — govern & assure AI (security, compliance, evaluation, safety). (site)
- AI Startup Curriculum — turn it into a company — the startup operating school for technical founders (functional curricula × role pathways × stages). (site)
- AI Workforce Curriculum — work alongside the models (AI-assisted engineering, analysis, product, and team leadership). (site)
| Role | Focus | Repositories |
|---|---|---|
| Agentic AI Developer | LLM fundamentals, prompting & structured output, tool calling, retrieval basics, first agent, shipping | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| AI Evaluation Engineer | Trace instrumentation, trajectory & tool evals, LLM-as-judge, RAG evals, eval-gated CI/CD, online evals | 📘 Learning · ✅ Solutions |
| Agentic AI Engineer | Agent loop, frameworks, RAG & memory, multi-agent, evals, guardrails, production, harness engineering | 📘 Learning · ✅ Solutions |
| Applied AI Engineer | AI feature design, production prompting, RAG in product, orchestration, product agents, guardrails | 📘 Learning · ✅ Solutions |
| RAG & Retrieval Engineer | Ingestion & chunking, embeddings, vector DBs & ANN tuning, learning-to-rank, hybrid retrieval, reranking | 📘 Learning · ✅ Solutions |
| Forward Deployed AI Engineer | Customer discovery, integrating into enterprise stacks, evals on customer data, demo→production, field feedback loops | 📘 Learning · ✅ Solutions |
| Voice & Multimodal Agent Engineer | STT/TTS & speech-to-speech, latency & turn-taking, telephony/WebRTC, vision inputs, voice & multimodal evals | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| Agent Platform Engineer | Internal agent platforms: harnesses, tool & MCP registries, sandboxing, permissions, shared evals & tracing, paved roads | 📘 Learning · ✅ Solutions |
| Senior Agentic AI Engineer | Agent systems from an architecture, eval & observability infra, multi-agent at scale, reliability, cost | 📘 Learning · ✅ Solutions |
| AI Engineering Manager | Hiring & leveling AI roles, planning non-deterministic work, eval-driven delivery, vendor risk, teams that use coding agents | 📘 Learning · ✅ Solutions |
| Agentic Systems Architect | When not to use an agent, orchestration & memory architecture, eval harnesses, security, durable execution | 📘 Learning · ✅ Solutions |
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Choose a track from the table above, based on your experience and direction.
-
Clone its repository:
git clone https://github.com/ai-engineering-curriculum/agentic-ai-developer-learning.git cd agentic-ai-developer-learning -
Start with the first module and read its
README.md. -
Work through the exercises in each module.
-
Check the paired solutions repository for reference walkthroughs.
- Model APIs: Anthropic, OpenAI
- Agent frameworks: LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex
- Tools & protocols: Model Context Protocol (MCP), Pydantic, FastAPI
- Coding-agent harnesses: Claude Code, Codex CLI, Claude Agent SDK
- Retrieval: pgvector, Chroma, Qdrant, BM25 hybrid search, reranking
- Production: Temporal, Docker, Kubernetes, GitHub Actions
- Observability & evals: OpenTelemetry, Langfuse, LangSmith, Phoenix, Ragas, Inspect
- Safety & governance: OWASP LLM Top 10, NIST AI RMF, EU AI Act
See the Career Progression Guide for level descriptions, a skills matrix, promotion criteria, and specialist tracks.
Contributions are welcome across the organization:
- Fix broken links, stale references, or inaccurate explanations
- Add depth to modules, exercises, or projects
- Improve validation for runnable exercises
- Report issues or ideas via GitHub Discussions
- Follow the
CONTRIBUTING.mdin the specific repository you want to improve
Curriculum repositories are MIT-licensed unless noted otherwise. See the target repository's LICENSE file for authoritative terms.
- Issues: use the relevant repository's GitHub Issues
- Discussions: organization discussions
- Docs: Career Progression
Maintained by VeriSwarm.ai