A hands-on build guide for a LoRA adapter trainer with fused low-rank gradients and adapter-aware inference in pure NumPy — the kind of project that signals deep ML and systems expertise to hiring managers.
A hands-on guide to building a LoRA fine-tuning engine entirely in NumPy, covering low-rank decomposition, gradient computation, and adapter updates with zero deep-learning framework dependencies.
A step‑by‑step tutorial to build a speculative decoding inference engine in pure Python, demonstrating model parallelism, benchmarking, and production‑grade extensions.
A hands-on guide to building a mixture-of-experts router from scratch in Python, covering top-k gating, capacity factor scheduling, and load balancing loss — a CV-worthy project that signals real systems and ML engineering skill.
A step-by-step guide to building a real-time ML inference service with async processing, request batching, and observability — designed to demonstrate production systems engineering on your CV.