Hey all I work on production LLM systems, mostly taking messy AI ideas and turning them into stable backend architecture. Recently finished a recruitment AI that scores candidates using a hybrid RAG setup (vector search + rule-based ranking), added long term memory per recruiter and reduced OpenAI API costs by 28% by optimizing prompt structure and adding smarter caching. Most of my focus is on system design, retrieval strategy, inference cost control and keeping AI features stable under real load. If you're moving from demo to real product, happy to compare notes.