AI Engineering: LLMs, RAG & Agents
Architect, evaluate, and deploy scalable LLM systems, dense retrieval pipelines, and autonomous agentic workflows.
This course provides a comprehensive engineering path through transformer architectures, vector indexing, retrieval-augmented generation, and autonomous agent loops. Students transition from theoretical self-attention derivations and probabilistic search algorithms to production-grade deployment with low-latency serving engines, evaluation harnesses, and tool-augmented LLM architectures.
Curriculum
Two complete tracks. Study either or both — each has its own exam and certificate.
A mathematically rigorous foundation in sequence modeling, dense representation learning, probabilistic retrieval models, and algorithmic reasoning graphs for intelligent systems.
Rigorous mathematical formulation of self-attention mechanisms, structural position encodings, and autoregressive sequence probability distributions.
- Scaled Dot-Product and Multi-Head Attention Derivations Lab40 min
- Positional Encodings: Sinusoidal vs. RoPE and ALiBi Lab35 min
- Autoregressive Sampling Dynamics and Decoding Theory Lab35 min
Careers this prepares you for
- AI Systems Engineer
- LLM Solutions Architect
- Machine Learning Platform Engineer
- RAG Pipeline Specialist