RoomPriceGenie

AI Engineer

• Design and implement the knowledge architecture: taxonomy, content lifecycle, ownership, metadata, provenance, and correct-at-source workflows.
• Build reliable ingestion and synchronization pipelines across Notion and approved business systems.
• Develop retrieval, search, and context-assembly services for internal AI experiences and production agents.
• Establish evaluation for knowledge quality: coverage, freshness, retrieval relevance, groundedness, and failure modes.
• Partner with teams migrating and structuring high-value knowledge; make good content practices practical rather than bureaucratic.
• Work with Security and Architecture on permissions, PII boundaries, auditability, and governance-by-default.

AI Engineer – Knowledge

• Design, build, deploy, and operate production AI agents and workflows.
• Integrate models and workflows with APIs, databases, and internal systems.
• Create evaluation harnesses and test datasets to measure success metrics.
• Implement LLMOps foundations: tracing, monitoring, and alerting.
• Define autonomy gates and operational runbooks with domain owners.
• Improve agents through production feedback and failure analysis.
• Build reusable agent components for safe shipping on a shared paved road.
• Stay hands-on in code, system design, debugging, and technical decision-making.

AI Engineer – Knowledge

AI Engineer – Agents