You are discussing a past AI engineering project you led or heavily influenced, such as deploying an inference service on NVIDIA Triton Inference Server, building a RAG workflow with NVIDIA NIM microservices, or shipping a model optimization effort with TensorRT. The interviewer wants to understand how you executed the work end to end, especially how you aligned stakeholders, made roadmap decisions, handled trade-offs, and managed delivery risk.
Walk me through one prior project you owned or drove. How did you scope it, align the right stakeholders, decide what to ship first, handle trade-offs as new information emerged, and keep delivery on track when risks showed up?