AI Engineer - Memory

• Build specialized agents within multi-agent pipelines designed for handling complex research problems, developing durable outputs, and taking action in external systems on the user's behalf.
• Build LLM-based memory capabilities: conversation summarization, cross-session context retrieval, and persistent user preference extraction.
• Design and implement extraction pipelines that identify durable facts and preferences from conversations.
• Build context assembly systems that provide agents with relevant user history and memory. Instrument pipelines for observability, tracing, and quality monitoring.
• Collaborate with evaluation engineers on quality measurement and improvement.

• 3+ years of experience building production Python systems. Experience with LLM applications: agent orchestration, prompt engineering, RAG, or similar. Hands-on experience with agent frameworks (LangGraph, LangChain, CrewAI, AutoGen, or similar).
• Understanding of LLM reasoning patterns and common failure modes.
• Proficiency with modern Python web frameworks.
• Comfort working in a fast-moving team where priorities evolve.

• Experience with semantic search, embeddings, and vector databases (e.g., Snowflake Cortex, Pinecone, Weaviate).
• Familiarity with LLM observability tools. Background in NLP, text summarization, or information extraction.
• Experience building systems that maintain user context or state over time.
• Familiarity with enterprise data systems (analytics APIs, data warehouses, SQL).

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