I'm an engineering leader with 12+ years of experience building distributed systems at high-growth startups like ixigo and Synaptic, systems serving millions of users and processing terabytes of data. Since 2023, I've been building with AI in production: semantic search over petabyte-scale data, LLM-powered pipelines, embeddings, vector databases, and agentic systems. Not theory, but systems that ship. Along the way I noticed most engineers are working with AI daily but have only a fragmented understanding of how it works underneath. That becomes a real limitation when you're designing agent architectures, debugging LLM behavior, or making build-vs-buy decisions. So alongside building, I run Agentic AI Mastery, a 20-module weekend cohort that takes software engineers from first principles (Transformers, embeddings, attention) to designing and shipping production-grade AI agents. Book a 1:1 for: A structured AI/ML learning path: what to learn, in what order, what to skip Reviewing your agent/LLM system design or architecture decisions Transitioning into AI engineering roles Distributed systems and engineering leadership Straightforward, practical conversations. You'll leave with clear next steps.