If you're looking for practical, common-sense advice on Generative AI application development, cloud architecture, or scaling real-world systems, you're in the right place. I’ve spent years designing and deploying resilient cloud infrastructure, building AI solutions that actually integrate with the system that yield real business value. I don’t just focus on theory—I help teams navigate the real challenges of implementation. Whether you need insights on cloud-native architectures, MLOps, or making large language models work within constraints, I can help you cut through the noise and get to what really works. Here’s what I bring to the table: 1. AI Strategy Consultation A focused advisory session where I help you navigate the complex AI landscape and develop a practical implementation roadmap. We'll cut through the hype to identify real opportunities for your business. Perfect for leadership teams trying to understand how AI can create genuine business value without getting lost in technical jargon. I'll share battle-tested frameworks for evaluating AI opportunities and help you prioritize initiatives based on feasibility and impact. 2. Technical Architecture Review A deep dive into your existing or planned AI system architecture. I'll evaluate your current approach for scalability, security, and production readiness, identifying potential bottlenecks before they become expensive problems. Drawing from my experience building systems that process terabytes of sensitive data, I'll help you design cloud-native infrastructure that balances performance, cost, and maintainability. Ideal for technical teams preparing to scale AI prototypes to production. 3. LLM & RAG Implementation Workshop A hands-on session focused specifically on implementing Large Language Models and Retrieval-Augmented Generation systems. We'll work through the practical challenges of LLM integration, from prompt engineering to evaluation frameworks. I'll share real-world techniques for improving accuracy, reducing hallucinations, and optimizing performance based on systems I've built that actually work in production environments. Best for engineering teams actively building GenAI applications who want to avoid common pitfalls.