I work at the intersection of Generative AI, Retrieval-Augmented Generation, and Agentic AI — helping professionals and organisations move beyond demos and build AI systems that actually work in production. My approach is grounded in a single conviction: the teams that understand the system will always outperform the teams that only know the tools. The Foundation — Data Science, ML & Deep Learning You can't architect a reliable AI system without understanding what's underneath it. My training builds from the ground up — data pipelines, feature engineering, classical ML algorithms, and the deep learning principles that underpin every modern AI system. This isn't background filler. It's what separates professionals who can use AI from professionals who can fix it when it breaks. Transformers & Large Language Models — how they actually work Before you can trust a model's output, you need to understand how it produces it. I cover Transformer architecture, attention mechanisms, tokenisation, fine-tuning strategies, and the emergent behaviours that make LLMs powerful — and unpredictable. When your model hallucinates, you need to know whether the problem sits in the weights, the context, or the retrieval layer feeding it. Most professionals never learn this distinction. My training makes it Day 1. NLP & Computer Vision — applied intelligence at scale My background spans both language and vision — from named entity recognition and sentiment analysis to object detection and image classification. These disciplines are increasingly converging inside multimodal systems, and professionals who understand both have a decisive edge in designing modern AI pipelines. Generative Models — beyond the API call I help professionals develop a working understanding of how generative models produce output — not just how to prompt them. That means understanding context windows, token generation, temperature behaviour, and the failure modes that only appear in production. When your model gives a wrong answer, you need to know whether the problem is the model, the context, or the system around it. RAG — where most AI systems silently break Retrieval-Augmented Generation is the most misunderstood layer in enterprise AI. Most teams treat it as a simple search-and-inject step. My training treats it as the critical system it actually is — covering retrieval precision, chunking strategy, embedding quality, reranking, and evaluation pipelines. If your team can't audit what's being retrieved, they can't trust what the model outputs. This is the gap most AI courses never close. It's where Grokkers starts. Agentic AI — building systems that reason and act Agentic systems represent the frontier of applied AI — systems that don't just respond, but plan, decide, and execute across multi-step workflows autonomously. My training covers agentic architecture from first principles: how agents maintain state, how they use tools, how they recover from failure, and how you evaluate a system that produces no single deterministic output. This is not a framework tutorial. It is systems design for AI that operates in the real world. The Philosophy The foundation is ML and DL. The architecture is Transformers. The application is GenAI, RAG, and Agentic systems. And the philosophy — from day one — is systems thinking over tool chasing. Across 450+ training batches and 60,000+ professionals trained globally, I've seen the same pattern play out repeatedly: teams that understand the system outperform teams that know the tools — every single time. My goal is to give your team that systems-level foundation, whether they're building their first ML model, deploying a RAG pipeline into production, or architecting a multi-agent system at scale. Whether you're an individual practitioner looking to deepen your understanding, or an organisation looking to upskill an entire AI team — I'll meet you where you are, and take you where you need to go. If you want a trainer who walks you through documentation, I'm not the right fit. If you want someone who helps your team think at the level of the system — and build AI that holds up — let's talk. → grokkers.com