Master AI Engineering
A production-grade curriculum for engineers who build, ship, and operate AI systems.
Most AI courses teach you to call an API. This curriculum teaches you what happens between the API call and the user - and how to keep it running at scale.
The Curriculum
12 modules. From LLMOps fundamentals to production evaluation - every layer of the AI engineering stack.
LLMOps and Infrastructure
Who it's for: Engineers deploying LLM-powered applications into production environments.
| Module | Topics |
|---|---|
| 01 - LLMOps | LLM lifecycle, deployment pipelines, versioning, cost management, operational maturity |
| 02 - AI Observability | Tracing LLM applications, latency monitoring, token analytics, debugging chains |
| 03 - LLM Gateways and Routing | Gateway architecture, model routing, load balancing, fallback strategies |
Data and Models
Who it's for: Engineers working with training data, model optimization, and AI security.
| Module | Topics |
|---|---|
| 04 - Synthetic Data Generation | Data augmentation, LLM-generated datasets, quality filtering, domain-specific synthesis |
| 05 - Model Compression | Quantization, pruning, distillation, GGUF, serving compressed models |
| 06 - AI Security | Prompt injection, jailbreaks, data poisoning, adversarial attacks, defense patterns |
Production Patterns
Who it's for: Senior engineers designing AI products and human-AI systems.
| Module | Topics |
|---|---|
| 07 - Production AI Patterns | Context management, caching, retry strategies, graceful degradation |
| 08 - AI Product Engineering | AI product design, UX patterns, feedback loops, feature flagging AI |
| 09 - Human-in-the-Loop Systems | HITL architecture, active learning, annotation pipelines, escalation design |
Applied AI Engineering
Who it's for: Engineers building RAG systems, prompt pipelines, and evaluation frameworks.
| Module | Topics |
|---|---|
| 10 - Prompt Engineering | Prompt design, chain-of-thought, few-shot, prompt testing, template management |
| 11 - RAG Engineering | Retrieval pipelines, chunking strategies, reranking, hybrid search, evaluation |
| 12 - AI Evaluation | Benchmark design, LLM-as-judge, human evaluation, regression testing, A/B testing |
What You Will Be Able to Do
After completing this curriculum:
- Deploy LLM applications with proper observability, gateways, and cost controls
- Design RAG systems that actually retrieve the right context - not just the nearest embedding
- Secure AI systems against prompt injection, data leakage, and adversarial inputs
- Build evaluation pipelines that catch regressions before your users do
- Architect human-in-the-loop systems that improve with every interaction
The Engineering Standard
Every lesson in this curriculum:
- Starts with a real production failure or design decision you will face
- Covers the architecture - not just the API calls
- Includes working code with production-grade patterns
- Ends with design questions that test systems thinking
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- AI Engineer
- LLM Engineer / LLMOps Engineer
- AI Platform Engineer
- ML Infrastructure Engineer
- AI Product Engineer
Certification (Coming Soon)
EngineersOfAI - AI Engineering Certification
Practical. Production-focused. Industry-aligned. For engineers who build AI systems that work - not just demos that impress.