أحدث منشورات Brij Kishore Pandey على لينكدإن

For years, most conversations stopped at: CPU vs GPU. But modern AI infrastructure is much bigger than that now. Today, different processors solve very different parts of the AI workload. CPU handles orchestration, preprocessing, operating system tasks, and general-purpose logic. GPU handles massive parallelism,…

The cloud is not really a cloud. Behind every AI response, API call, banking transaction and streamed video, there is physical infrastructure doing the work. And much of it begins inside a rack like this. When a request reaches a data center: → The patch panel organizes the physical connections → The switch conne…

Two months ago, this “AI Factory Stack” graphic went viral. Looking at it again, the most important insight isn’t any single layer. It’s where the competitive advantage is moving. Models are improving quickly—and becoming easier to replace. But the system surrounding the model is much harder to copy: • The context…

Two months ago, this “AI Factory Stack” graphic went viral. Looking at it again, the most important insight isn’t any single layer. It’s where the competitive advantage is moving. Models are improving quickly—and becoming easier to replace. But the system surrounding the model is much harder to copy: • The context…

Two years ago, backend engineering looked very different. Most of the work was around: APIs Databases Queues Caching Auth Cloud Security Observability That foundation is still extremely important. But now, a new layer is being added on top of it. LLM APIs. Embeddings. Vector databases. RAG. Context engineering. To…

The moment an AI agent can take action, your engineering responsibilities multiply. You have to decide what it knows, what it can access, how it recovers, and when it must stop. That’s why I find it useful to think about AI systems engineering across seven layers: 1. Foundation Model → Capability Choose a mod…