What we need to see: 8+ years in applied ML/AI or large-scale systems, with 2+ years crafting agentic or LLM-powered applications in production environments. B.Sc in Computer Science / Electrical Engineering. Solid grounding in computer architecture: memory hierarchies, parallelism models, pipelining, and cache behavior. Specific familiarity with NVIDIA GPU architecture — streaming multiprocessors, warp scheduling, shared/global memory model, and occupancy reasoning — is essential. Hands-on CUDA programming experience: writing, profiling, and optimizing GPU kernels — not just calling into CUDA-accelerated libraries. Comfortable with tools such as Nsight Compute, Nsight Systems, or equivalent profiling workflows. Proven ownership of at least one end-to-end agentic system or LLM application: requirements, architecture, implementation, evaluation, and incremental hardening in production — not just experience with off-the-shelf frameworks. Strong software engineering skills in Python and one systems language (C++
