AI Software Engineering
Fullstack development evolution. LLM-native system architecture, RAG pipeline implementation, and design patterns for cognitive software.
AI Engineering LLM Ops System Design
Software Engineering for the Cognitive Era
The paradigm has shifted: from writing deterministic code to orchestrating probabilistic systems. This program is a deep dive into the engineering required to build reasoning applications.
Core Curriculum
- LLM-Native Architecture: Design patterns for integrating language models into production systems (Chain of Thought, ReAct).
- Advanced RAG (Retrieval-Augmented Generation): Hybrid vector indexing strategies, semantic re-ranking, and context evaluation.
- Agent Engineering: Designing autonomous systems capable of using tools (Function Calling) and planning complex tasks.
- LLMOps and Evaluation: CI/CD pipelines for AI, prompt management as code, and evaluation frameworks (Ragas, TruLens).
Professional Profile
We train AI Engineers who understand both the complexity of distributed software development and the nuances of stochastic behavior in generative models.