Responsible AI Engineering: Alignment, Safety, and Governance

As AI systems move into high-stakes domains, responsible AI engineering is becoming a core requirement, not an afterthought. Building models is no longer enough; engineers must ensure systems are aligned, robust, and safe under real-world conditions. This course provides a structured path to understanding and applying responsible AI engineering in modern AI workflows. I built this course from my work in adaptive AI systems and intelligent architectures, where model reliability and alignment are critical. A recurring pattern I observed was that teams focused on performance metrics while overlooking failure modes such as bias, misalignment, and unsafe behavior. This course addresses that gap by framing responsible AI engineering as a system-level discipline. You’ll explore AI risks, alignment failures, interpretability techniques, and adversarial testing. The course covers tools like LIME, SHAP, and red-teaming workflows, along with alignment strategies and runtime governance. If you want to apply responsible AI engineering in real systems, this course provides a clear, practical foundation.