I designed the core reference architectures behind Rasa University, translating Rasa’s agent capabilities into a scalable certification and enablement pathway for architects, developers, partners, clients, and internal teams. The work bridged AI system design, technical implementation, product education, and GTM communication, turning complex conversational AI architecture into practical learning flows, reference patterns, and guidance for adoption at scale.
Large Language Models are a rapidly evolving domain, and I led the development and deployment of a chat application designed to interact with hundreds of patent data documents. To mitigate hallucinations, I implemented retrieval-augmented generation, ensuring the LLM’s responses were constrained to specific data stored in vector databases on cloud platforms such as Qdrant.
I developed and deployed advanced AI agents to 14,000+ DataCamp subscribers, showcasing how to rapidly prototype and orchestrate chatbot and language-based AI applications using LangChain and LangGraph. I guided users through robust development pipelines in Python, emphasizing practical implementation of both foundational and advanced tools for building multi-step agents capable of handling user queries and executing autonomous tasks.
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