Production-Grade AI Engineering Curriculum

Four core modules and a capstone over 16 weeks, moving from LLM foundations through search and RAG, agents and automation, to production deployment, preparing you for real-world AI roles.

Mastering the AI Engineering Stack

Module 1: LLM Foundations

Learn how LLMs work, including tokens, context windows and embeddings. Build with the OpenAI and Claude APIs, write reliable prompts, get structured outputs with Pydantic, and evaluate results with test sets and LLM-as-judge.

Module 2: Search and RAG

Build RAG systems that answer from real documents. Cover document parsing, chunking, BM25 and vector search with Qdrant and pgvector, hybrid search and reranking, Graph RAG, and multimodal RAG evaluated with RAGAS.

Module 3: Agents and Automation

Build AI agents and multi-agent systems with LangGraph. Connect tools through MCP, automate workflows with n8n, trace agents with Langfuse or LangSmith, and secure them with guardrails against prompt injection.

Module 4: Production Deployment

Ship AI services with FastAPI, Docker, CI/CD and AWS. Monitor latency and cost, reduce spend with caching and model routing, and run and fine-tune open models with Ollama, LoRA and QLoRA.

Capstone and Career

Design and build a complete AI system, then present it on demo day. Get job-ready with a strong GitHub portfolio, clear READMEs and LinkedIn positioning.

Curriculum FAQs

How long is the course?

Do I need coding experience?

Is an internship included?

The intensive AI Engineering program spans 4 months, culminating in a 4-6 week internship.

Yes, a strong foundation in Python is required. We build on these fundamentals to develop production-ready AI systems.

Absolutely. Every student participates in a 4-6 week internship to apply their skills in a real-world setting.

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Ready to Engineer Your Future?

Join our next cohort and build production-ready AI systems that companies actually hire for.