Software Engineering & Architecture with AI

Detailed Curriculum · 160 hours (120 traditional + 40 AI-augmented)

Prerequisites: Basic knowledge of at least one programming language (preferably C#, Java, Python, Go, or TypeScript) and general familiarity with software development practices. Basic knowledge of Git and cloud technologies is beneficial but not required. No prior AI/LLM experience is required.

Positioning: this course teaches the discipline of building production software properly — then spends its last 40 hours going back through exactly what was built and showing precisely where AI collapses the timeline and where it doesn't. Every AI-augmented exercise points at a specific artifact (an ADR, a test suite, a legacy module) the learner produced by hand earlier in the course.

Outline
1. Foundations at Scale - 30 hours - SOLID, design patterns, Domain-Driven Design, C4/ADR discipline
2. Architecture & Design - 30 hours - HLD/LLD, microservices vs. modular monolith, API-first design, event-driven architecture, CQRS
3. Data & Distributed Systems - 30 hours - Data modeling, distributed systems fundamentals, messaging, caching
4. Quality, Security & Delivery - 30 hours - TDD, code review discipline, authN/authZ, secure-by-design, CI/CD
5. AI-Augmented Engineering - 40 hours - AI-assisted design, implementation, testing, review, and legacy modernization

Total: 160 hours

Delivery model

This course is taught in 4-hour slices. Each module is broken into 4-hour delivery blocks with a short recap/reflection segment at the end to absorb the design decisions and lab outcomes.