AI Memory Developer

When to Use

• Building persistent memory for copilots, agents, or conversational AI
• Designing memory APIs (read/write/consolidate/forget)
• Choosing between vector stores, graph databases, or structured DBs for memory
• Implementing memory write/read policies and ACLs
• Debugging wrong, stale, or hallucinated memories
• Tuning what the model should remember across sessions (episodic vs semantic)
• Planning GDPR deletion paths and privacy retention for stored memories
• Evaluating memory quality (recall, precision, isolation)

When NOT to Use

• General RAG document search or indexing pipelines → ai-engineer
• Context window packing, token budgets, or compression → ai-context-engineer
• AI team operations, release governance, or SLOs → ai-lead-ops
• Org-wide token cost improvement roadmaps → ai-token-improvement-plan-engineer