The AI Engineer Skill Roadmap

Best practical async Python content on YouTube — starts with why, not just how

The gold standard API framework for AI services — async native, Pydantic-integrated

Best end-to-end Docker course — covers everything from local dev to production deployment

Production-ready FastAPI template — study this to understand how production AI backends are structured

Essential for production AI — validate all LLM inputs and outputs, generate JSON schemas, integrates natively with FastAPI and structured outputs

Used at Anthropic and leading AI labs — 10-100x faster than pip, lockfile support

Written by the team that trained Claude — the most authoritative prompt engineering reference available

Andrew Ng + Isa Fulford — 1-hour fundamentals course used by over 1M engineers

20k+ GitHub stars — the production standard for getting typed, validated output from any LLM

Single interface to call 100+ LLM providers — essential for model routing and fallbacks

Hands-on code examples from the Claude team — prompt caching, tool use, streaming, multimodal inputs, and model routing patterns

Comprehensive open-source guide covering every prompting technique with benchmarks

The definitive short course on production RAG — covers evaluation, not just building

35k+ stars — the most comprehensive RAG framework with 200+ connectors

Production vector DB used by Notion, Hubspot, Gong — has a generous free tier for development

Use this if you already run Postgres — no new infrastructure, surprisingly competitive performance

The standard eval framework for RAG — measures faithfulness, answer relevancy, context precision

The production-standard reranker — adds a cross-encoder pass on top of vector search, lifting precision by 20-40% on real benchmarks. Free tier available

The most practical course on production-grade agents — covers state machines, memory, HITL

The production standard for stateful agent orchestration — used at Replit, Elastic, and others

First-party documentation on Claude tool use — the reference implementation

22k+ stars — role-based multi-agent framework, good for rapid prototyping

Anthropic's definitive guide to agent design — required reading before writing a single line of agent code

Fast path to multi-agent concepts with real production patterns

The fastest-growing LLMOps tool — open source, self-hostable, integrates with every framework

Best-in-class if you use LangChain/LangGraph — evaluation and tracing in one platform

2-hour course on end-to-end LLM deployment pipelines with Google Cloud

Open source alternative to Langfuse — excellent traces UI, built-in evals

Industry standard for fine-tuning experiments and model versioning at ML-heavy companies

CLI-first prompt evaluation and CI/CD regression testing — compare providers, catch quality regressions before deployment, built-in red-team mode. 5k+ stars

4.5k+ stars — production guardrail framework with 50+ validators for common LLM failure modes

pytest-style LLM testing — write evals like unit tests, run in CI/CD

Practical 2-hour course on building safety guardrails and quality monitoring

How frontier labs think about safety thresholds — essential reading for understanding the domain

NVIDIA's production guardrail framework — define safety policies in Colang, enforce conversational guardrails, prevent off-topic and harmful outputs. Enterprise-grade

The paper defining faithfulness, answer relevancy, context precision — the language of LLM eval