Filip Makraduli is a machine learning engineer and developer advocate with a strong background in AI systems, vector search, and large language models (LLMs). He holds a Master’s degree in Biomedical Data Science from Imperial College London. Currently, Filip works as a founding developer relations engineer at Superlinked, where he focuses on building real-time, multi-attribute search and recommendation systems. His work emphasizes the use of multi-encoder architectures to enhance retrieval quality and reduce reliance on reranking strategies. In the past, Filip worked as a data scientist at Marks & Spencer, where he contributed to AI-driven solutions for retail. He has also held machine learning engineering roles across several UK-based startups, focusing on applied AI and product-oriented ML development. In addition to his industry work, Filip has been active in the open-source community, particularly around LLM tooling and pipelines. He has delivered various talks on practical machine learning applications, including a presentation on AI-powered music recommendation systems titled “When music just doesn’t match our vibe, can AI help?” Filip is passionate about bridging the gap between cutting-edge AI research and real-world applications, particularly in the areas of personalization, search, and recommendation systems. He also has a strong interest in the business side of technology, especially how product, research, and engineering decisions align with go-to-market strategies, developer adoption, and long-term commercial value.