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AI Engineer courses

AI engineers build the systems that put LLMs into production. RAG pipelines that retrieve the right context, agents that call tools reliably, evaluation harnesses that catch regressions before your users do. It's backend engineering with a new stack, not prompt engineering with a fancy title.

Curated by Param Harrison

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These courses teach the concrete patterns. Embeddings and vector stores, retrieval architectures, agent loops, MCP, LLM observability, and the failure modes you hit at scale. Every course ships with real code you can run locally, not slides you watch.

Showing 46 of 46 courses

{}defai()

Python for GenAI Engineering

Every AI tutorial assumes you know Python. This one doesn't.

BeginnerFree
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QKV

GenAI Masterclass: Go from Zero to One

6 hands-on projects. 6 core AI skills. Zero fluff.

NoobsFree
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Prompt Engineering Crash Course

The prompting techniques senior AI engineers use daily, and most tutorials skip.

NoobsFree
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LLM

RAG Fundamentals for Everyone

Master the core building blocks of RAG, from embeddings to agentic retrieval.

BeginnerFree
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AItoolsloop

Learn AI Agents from Scratch

Build AI agents with pure Python. No LangChain. No CrewAI. No magic.

BeginnerFree
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RAGfndbapi

Advanced RAG Course: Build Text to SQL Agentic AI System

Build production agentic AI systems, from architecture to deployment.

AdvancedProHot
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orchestrator

Advanced Design Patterns in AI Agents

Battle-tested agent patterns. The reference guide that didn't exist until now.

AdvancedFree
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clientMCPserver

MCP Fundamentals for Engineers

Build MCP servers and clients from scratch. No more glue code.

BeginnerFree
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Learn to Build your own Coding Agent (Claude Code)

Reverse-engineer how Claude Code works. Then build your own production AI coding agent from scratch.

AdvancedPro
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Build your own AI assistant from scratch

Write your own AI assistant in Python, one short file at a time. From one API call to a multi-agent system that remembers you, runs commands safely, and keeps its own schedule.

IntermediatePro
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word_

Streaming LLM Applications with FastAPI

Stream LLM responses in real-time and master prompt engineering fundamentals.

BeginnerFree
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Building persistent AI assistants with Telegram

Build a Telegram AI assistant that remembers, uses tools, and runs its own background tasks.

IntermediateFree
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Structured data extraction with vision LLMs and Pydantic

Extract structured data from images with vision LLMs and Pydantic validation.

IntermediatePro
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Image-to-image generation with FLUX and provider pattern

Build an image-to-image API with FLUX and the provider pattern.

IntermediatePro
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Multi-agent document analysis with LangGraph

Orchestrate specialized agents with LangGraph to analyze documents at production quality.

AdvancedPro
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Hybrid document search with Qdrant and Sentence Transformers

Hybrid retrieval with dense vectors, sparse keywords, RRF fusion, and cross-encoder reranking.

IntermediatePro
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RAG systems with ChromaDB and cross-encoder reranking

Build a grounded RAG chatbot with ChromaDB, web scraping, and cross-encoder reranking.

IntermediateFree
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EVAL

Advanced RAG with query rewriting and evaluation

Add query rewriting, sub-graphs, PII scrubbing, and RAGAS scoring to a production RAG pipeline.

AdvancedPro
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Multi-agent systems with AutoGen GroupChat

Orchestrate specialist agents with AutoGen GroupChat and a supervisor router.

AdvancedPro
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Building voice AI agents with LiveKit and Deepgram

Build a real-time voice AI agent with LiveKit, Deepgram, and tool calling.

IntermediatePro
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Multi-agent voice systems with LiveKit

Orchestrate specialized voice agents that hand off conversations without losing context.

AdvancedPro
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Search-augmented generation with web scraping

Search the web, extract article content with BeautifulSoup, and synthesize cited answers with LLMs, like building a mini Perplexity.

IntermediateFree
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TEXT

Local voice transcription with Whisper and LLM post-processing

Record audio in the browser, transcribe locally with Whisper, and clean output with an LLM pipeline.

IntermediatePro
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Vectorless RAG with Hierarchical Document Trees

Skip the vector database. Navigate document trees with an LLM agent and get cited answers.

AdvancedFree
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Building RAG applications with Next.js and the Vercel AI SDK

Implement direct LLM chat, native tool RAG, and LangChain vector RAG in one Next.js app with the Vercel AI SDK.

IntermediateFree
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Video QA with transcript search and timestamp citations

Build a grounded YouTube video Q&A assistant with hybrid retrieval and clickable timestamps.

IntermediatePro
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Long document RAG with conversational memory

Build a conversational AI tutor over long PDFs with chapter-aware chunking and memory-backed retrieval.

IntermediatePro
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LLM Observability with Arize Phoenix

Emit JSON logs with request IDs and OpenTelemetry spans across every tool call so you can click a trace and see exactly what your agent did.

AdvancedPro
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Building multi-provider LLM apps with OpenRouter

Swap OpenAI, OpenRouter, Gemini, and Ollama behind one chat() function with automatic fallback.

IntermediatePro
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Stateful agent workflows with LangGraph

Build a long-running, multi-turn agent with typed state, intent routing, and checkpointed memory.

IntermediatePro
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Conversational state machines with LangGraph

Model multi-turn chat as a typed LangGraph state machine with streaming and thread memory.

IntermediatePro
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Building MCP servers for GitHub PR review

Build an MCP server that reviews GitHub pull requests end to end.

IntermediatePro
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Full-stack agentic AI with Next.js and LangGraph

Ship a multi-step AI agent in one Next.js app with streaming tools and memory.

AdvancedPro
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SUPERVISOR

Supervisor-routed multi-agent systems with LangGraph

Route user messages to specialist subagents with a LangGraph supervisor and stream each one over SSE.

AdvancedPro
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Sentiment classification with LLMs and few-shot prompting

Build a multi-task NLP service powered by focused LLM prompts and per-task streaming.

BeginnerPro
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Graph RAG with LangChain and Neo4j

Build a knowledge graph from raw text, ask natural-language questions over it, and beat vector RAG on multi-hop queries.

AdvancedPro
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main.go

Building coding agents from scratch in Go

Build a zero-dependency coding agent in Go with tool calling, safety guards, and a distroless binary.

AdvancedPro
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role: adminLLM

Build an RBAC-gated RAG chatbot

Role-gated RAG chatbot: per-role document walls, session auth, rate limits, and an admin panel.

IntermediatePro
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Real-time phone agents with FastRTC

Ship a real-time phone voice agent over WebRTC with FastRTC, Whisper, and swappable TTS.

AdvancedPro
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Layered production AI architecture

Architect an agent as seven composable layers with per-request traces.

AdvancedPro
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87/100AI

Agent evaluation techniques

Stop shipping agents you cannot defend. Learn the eval patterns LangSmith-backed teams actually use.

AdvancedPro
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AItracelangfuse

Production agentic systems with Langfuse

Turn a notebook agent into a service you would be happy to wake up to at 3am.

AdvancedPro
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runmodellatencycostp50 / p95

LLM observability with MLflow

Stop guessing what your agent did. Trace every step with the open-source observability tool ML teams already use.

IntermediatePro
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supervisoragent_atoolagent_bretrievellm0ms1.2sotel trace

Multi-agent tracing with OpenTelemetry and Phoenix

Stop debugging supervisor handoffs by guessing. Trace every agent, every tool, every routing decision.

AdvancedPro
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queuevllm batchgpu

Serving LLMs at scale with vLLM, quantization, and Kubernetes

When the API bill outgrows a managed provider, you self-host. Here is the production path that actually scales.

AdvancedPro
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uitems#1#2#3#4#5

Personalized recommender systems: Two-tower retrieval to production serving

Build a four-stage personalized recommender: two-tower retrieval, ranking, optional LLM rerank, served by FastAPI on local Postgres, Qdrant, and MLflow.

IntermediatePro
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Common questions

AI Engineer: quick answers

  • What does an AI engineer actually do day-to-day?

    Write code that wires LLMs to tools and data. You’ll spend most of your time on retrieval logic, agent loops, evaluation harnesses, cost monitoring, and the integration glue between model providers and your stack. Prompt writing is maybe 10% of the job.

  • Do I need a machine-learning background?

    No. AI engineering is a distinct path from ML research. You need strong software engineering fundamentals (APIs, typed data, concurrency, databases, observability) plus comfort with the LLM API surface. You do not need to train models to ship production AI.

  • Python or TypeScript for AI work?

    Python for the backend + pipelines (better SDK coverage, eval tooling, data libraries). TypeScript or Next.js for the frontend and streaming surfaces. Most production teams run both. The courses on this track use Python for engine work and Next.js for UI.

  • How is this different from prompt engineering?

    Prompt engineering is one tactic inside AI engineering. An AI engineer designs the whole system: retrieval, tooling, memory, guardrails, deployment, monitoring. A prompt engineer tunes strings. Companies hire engineers, not prompt tuners.

  • What’s the fastest path if I’m already a backend engineer?

    Start with Python for GenAI (if new to Python) or jump straight to RAG Fundamentals. Follow up with AI Agents Fundamentals, then the production-focused courses: observability, agentic RAG, and the coding-agent masterclass. Treat it as a part-time arc alongside your day job and you’ll be shipping by the end.

Related paths

RAGAI AgentsMCPPythonLLM Observability
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