ashby
Remote AI Systems & Automation Engineer (Internal Operations) (m/f/d)
RoomPriceGenie
Remote, AUS
Full-time
Senior
Remote
EUR 85000-95000 yearly / year
Discovered 1 weeks ago
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Why RoomPriceGenie?
We take both culture and product seriously: recognized as Best Place to Work in HotelTech (2026) and Best Revenue Management Software (2026) . We power pricing for thousands of hotels worldwide on a modern data stack.
This is a fully remote role within Europe (UTC+0 to UTC+2) . You will have genuine ownership, strong support, and the opportunity to build high-value internal AI systems and agents that make our operational teams significantly faster and smarter.
Your Role
- As an AI Systems & Automation Engineer , you will drive internal AI adoption across Customer Success, Sales, and Operations. Your job isn't to build raw ML models or write throwaway demos. You will work directly with business leads to unblock legacy processes, connect internal data to core operational tools, and deploy dependable, supervised AI agents that automate complex workflows.
- You will join our AI & Security team and bridge the gap between our core data platforms (Snowflake, Postgres, DocumentDB, S3) and our GTM stack (HubSpot, Chargebee, Modjo, Notion), establishing practical patterns for safe, agentic automation across the business.
What You’ll Do
Translate Business Pain into Systems: Partner directly with Customer Success, Sales, and Ops teams to map out manual, multi-step workflows and design high-ROI AI automation targets.
Build Production Agents & Integrations: Write clean, maintainable code (Python/TypeScript) connecting LLM APIs to internal data stores and third-party SaaS platforms (HubSpot, Chargebee, Modjo, Notion).
Implement Modern Agent Standards: Build modular agent capabilities using standardized tools and schemas (such as Agent Skills standards, Claude Plugins, or custom function-calling pipelines) to give models safe, structured action boundaries.
Design Human-in-the-Loop Workflows: Implement strict guardrails, schema validation, human approval gates, and escalation paths so non-technical staff can safely supervise AI outputs.
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