Daniel Cotrim

I am an AI and Graph Data Science specialist with over 15 years of experience in machine learning, NLP, LLMs, and agentic architectures. I currently lead a knowledge synthesis platform that integrates state-of-the-art LLMs, RAG systems, and grounding techniques over Neo4j graphs to deliver contextual, auditable insights for critical domains.

I have proven hands-on expertise in fine-tuning and production deployment of AI models across AWS, Azure, and GCP with a strong emphasis on scalability, reliability, and performance. I design distributed multi-agent architectures orchestrated via Redis, enabling dynamic code execution loaded from knowledge graphs for rapid experimentation and continuous validation.

My data-first approach uses real usage metrics, feedback loops, and Graph Data Science techniques such as PageRank, community detection, and link prediction to optimize resources and recommend features. At my current platform this methodology reduced experimentation cycles from days to minutes and improved financial compliance analysis accuracy by over 35%.

I have contributed academically as a reviewer and technical committee member and taught Python and AI courses at undergraduate and postgraduate levels. I am motivated to apply my interdisciplinary background in data science, systems engineering, and business understanding to deliver high-impact, globally scalable AI solutions.