Ai Assistant Enhancement: Semantic Memory and Special Skills Development

We are seeking an experienced developer/consultant to enhance our next-generation AI assistant. Our assistant currently features a robust foundation, including established conversation flow, intent classification, emotional tagging, and user profiling capabilities. The primary goal of this engagement is to expand the assistant's functionalities by integrating advanced features:

1. Semantic Memory: Implement the ability for the AI assistant to recall and contextualize past conversations, enabling more coherent and personalized interactions over time.
2. Special Skills: Develop modular functions that allow the assistant to handle specific, complex tasks. Examples include profile enrichment, contextual recommendations, and dynamic conversation strategies.

We are looking for a professional with proven expertise in Large Language Models (LLMs), embeddings, and memory architecture. Experience with vector databases and memory management systems such as Supabase, Pinecone, Weaviate, or similar technologies is highly valued. The ideal candidate will provide hands-on guidance and design scalable, clear solutions. This is intended as an ongoing collaboration, not a one-off project, as we aim to continuously evolve our AI assistant.