AI Engineering Course
Designed to help software engineers transition to AI engineering, with detailed breakdowns of vector databases, indexing, large language models, attention, and core optimizations so you can understand how LLMs work and use them to build real-world applications.
What you'll learn
- Transition from software engineering to AI engineering
- Understand vector databases, indexing, and semantic search
- Learn how GPT-style large language models work internally
- Master attention, transformers, and core LLM optimizations
- Design and deploy RAG pipelines and AI agent architectures
- Apply LLMs to build and scale real-world AI applications
Skills you'll gain
- Build mental models for how GPT-style LLMs work
- Understand tokenization, embeddings, attention, and masking
- Optimize LLM inference using caching, batching, and quantization
- Design and deploy RAG pipelines with vector databases
- Compare prompting, fine-tuning, and agent-based architectures
- Debug, monitor, and scale LLM systems in production
- Apply core optimizations like paged attention, MoE, and flash attention
- Use reasoning techniques such as chain-of-thought and tool usage in LLMs
- Work with AI agents and Model Context Protocol in practical applications
Prerequisites
- • Basic software engineering experience
- • Familiarity with programming and distributed systems concepts
Who this course is for
- → Software engineers transitioning to AI engineering roles
- → Engineers preparing for higher-level system design and AI interviews
- → Developers who want to understand and use LLMs in production systems
Provider
InterviewReady
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