Dinesh Wadhwani
I built my career from the ground up as a software engineer, grew through multiple leadership roles in global technology organisations, and now teach what I've lived — both the engineering depth and the leadership breadth that the AI era demands.
From Engineer to Educator
My journey began as a software engineer driven by curiosity — not just about how systems work, but about how technology shapes industries and organisations. Over two and a half decades, I built, scaled, and led technology products across global companies, navigating everything from early-stage product development to enterprise-scale engineering challenges.
Through that work, I noticed a consistent gap: the most technically capable people often struggled to lead, to communicate, and to translate deep expertise into organisational impact. That observation pulled me toward coaching — first informally, then as a structured practice.
Today I sit at the intersection of deep software engineering credibility and executive leadership development. This AI course is where those worlds meet: rigorous technical education, delivered with the clarity and intentionality of someone who has had to make real decisions with real consequences.
Why This Course Exists
The AI landscape is moving faster than almost any educational institution can keep up with. Most courses on the market either stay safely theoretical — lots of slides about transformers and neural networks without ever building anything — or they jump to code without giving students the mental models to understand what they're building and why.
I built this course to be the one I wished existed when AI started transforming the software industry. Every concept earns its place by connecting to something real and buildable. Every session leaves you with an artifact — something you created, tested, and understand deeply because you made mistakes with it.
The running project (a hiring AI system that evolves across all 12 sessions) is intentional: AI is not a collection of techniques. It's a discipline of building systems that reason, retrieve, generate, and act. You can only understand it by building it, breaking it, and building it better.
How I Teach
Build First, Abstract Later
Every concept is introduced through a concrete building task. Intuition built from making something is more durable than intuition built from reading about it.
Mental Models Over Syntax
The frameworks for thinking about retrieval, generation, and agentic behaviour matter more than any specific API. Good mental models transfer across tools; syntax doesn't.
Progressive Complexity
Session 1 is accessible to anyone. Session 12 is genuinely challenging. The curve is calibrated so every student progresses — the project grows with you.
What I Bring to the Room
25 years of industry experience across multiple domains — always with engineering rigour and leadership context.
Start With Session 1
No AI background required — just curiosity and a willingness to build something real.