I specialize in Recommender Systems (RecSys)—designing, optimizing, and deploying models that personalize user experiences at scale. My focus is on efficient, scalable, and high-performing RecSys architectures, balancing relevance, diversity, and real-world constraints.
Beyond work, I’m passionate about Edge AI, Small LMs, and local inference—exploring how to make models leaner, faster, and deployable beyond the cloud. Always experimenting with quantization, distillation, and model optimization to push the limits of what’s possible in resource-constrained environments.
Big fan of efficient AI, ranking models, and lightweight ML deployments. Always open to chatting about RecSys, model optimization, and edge intelligence!