Defend an AI Infrastructure Project's Architecture Step by Step on a Flow Diagram

This round was a deep dive into a project from the resume rather than a new design problem. Choose a project you know end to end. Draw a flow diagram of its system from input to output, then walk through the diagram one step at a time, explaining the architecture of each step: what runs there, what it receives and produces, and how it hands off to the next step. Any step can be interrupted with a challenge: how did you think about this, why is it designed this way, and what is the trade-off? The probing goes far below the level of a summary, so expect to defend the details of the parts you built. The questions leaned toward AI infrastructure, the systems that move data to models, train them and serve them, so a project in that area is the natural choice if you have one, and the challenges are likely to concentrate there. ```hint Choose for depth, not size Pick the project in which you can explain every box you draw, including the options you rejected. In a round this deep, a large system you only partly understand works against you. ``` ```hint Prepare the why behind each step For each step in the diagram, have ready the alternative you considered, why it lost, what the choice cost, and a number that supported it. ``` ```hint Keep your place Decide in advance how you will get back to the diagram after an interruption, so that a challenge on one step does not derail the rest of the walk-through. ``` ### Clarifying Questions - Should I take one project in depth, or briefly outline two and let you choose? - Should the diagram show the system as it was built, compromises included, or as I would design it today? - Which part interests you most: the data path, training, serving, or the deployment and monitoring around them? - How deep should each step go: component level, or down to configuration and code? ### What a Strong Answer Covers - The whole flow drawn first, with the candidate's own ownership marked, before any step is explored in depth - For each step: the component, its inputs and outputs, its scale, and how it fails - For each challenged decision: the alternatives considered, the constraint that decided it, the cost accepted, and the evidence behind it - Depth where the project touches AI infrastructure, such as accelerator utilization, data throughput, batching, model rollout and cost - Measured results, and an honest account of what the candidate would change now - Composure under interruption: a direct answer to each challenge, then a clean return to the diagram ### Follow-up Questions - If load grew tenfold, which step in your diagram would break first, and what evidence tells you so? - Which design decision would you reverse if you rebuilt the system today, and what has keeping it cost? - If the system uses GPUs or other accelerators, where in this flow do they sit idle, and what would raise their utilization? - How does a new model or component version move through this flow into production, and how is it rolled back if it misbehaves?