Course Description:
Fundamentals of AI for Software Engineers equips software engineers with a foundational understanding of how Artificial Intelligence enhances software development, testing, deployment, and maintenance—without replacing core engineering discipline.
The course builds shared vocabulary, conceptual clarity, and applied awareness of where AI adds value in:
• Code generation
• Debugging
• Testing
• Documentation
• Architecture support
• Workflow acceleration
It also clarifies where human creativity, accountability, systems thinking, and engineering rigor must remain central.
The course introduces emerging ideas such as Agentic AI—AI systems that can take multi-step actions within defined boundaries—at a conceptual level. Engineers will understand what agentic systems are, how they differ from traditional AI assistance, and why governance, guardrails, and human oversight become even more critical.
A core theme throughout the course is Respect and Trust in AI-assisted engineering:
• Respect for engineering discipline
• Respect for users and stakeholders
• Trust as an engineered property—not an assumption
• Understanding how careless AI usage erodes system trust
This is not a tooling course. It does not teach how to build AI models. It establishes disciplined understanding so engineers can adopt AI responsibly and professionally.
Who Should Take This Course?
• Software engineers (junior to senior)
• Backend, frontend, and full-stack developers
• QA engineers transitioning into AI-augmented workflows
• DevOps engineers seeking foundational AI literacy
• Technical leads requiring structured AI understanding
• Computer science graduates entering professional roles
• Engineering managers who need conceptual AI awareness
Pre-Requisites:
• Basic understanding of SDLC concepts
• Familiarity with software engineering terminology
• Experience reading or writing code
• No prior AI or machine learning knowledge required
Course Outline:
Module 1: Foundations & Context
Module 2: Core Concepts & Terminology
Module 3: Where AI Fits and Where It Does Not
Module 4: Pillars and Practices
Module 5: Transformation Approaches — What Works and What Doesn’t
Module 6: Practical Use Cases for Software Engineers
Module 7: Organizational & Career Impact, Emerging Trends
Module 8: Review, Synthesis, and Exam Readiness
