Applied AI engineer in agentic infrastructure

Most property tech startups fail because selling SaaS into fragmented, legacy agencies is a slow grind. This company takes the opposite approach: they acquire independent operators outright and rebuild the entire operational layer on their own proprietary software. Backed by top-tier global VCs and growing rapidly through acquisitions, they now manage thousands of properties and turn manual legacy operations into automated, reliable software systems at scale.

• Architect the core platform for multi-agent orchestration, context management, tool integration, and state persistence across operational workflows.
• Build automated evaluation harnesses and observability tooling to benchmark agent reliability and catch regressions before systems go live.
• Standardise custom operational processes into modular, reusable agentic components so newly acquired businesses can be onboarded in days.
• Define the architectural boundary between autonomous agent execution, deterministic backend code, and human intervention safeguards.
• Leverage agentic software engineering practices and coding agents as a core part of your daily development cadence.

• Track record of shipping LLM-powered or agentic systems into real production environments, well beyond basic API wrappers, proof-of-concepts, or hackathon demos.
• Strong backend engineering foundation in TypeScript/Node.js, or deep Python experience with the ability to ship in a TypeScript-first codebase.
• Demonstrated architectural judgement on where to deploy probabilistic LLM agents, where to enforce deterministic logic, and where human-in-the-loop review is mandatory.
• Active, daily use of AI coding agents to decompose tasks, write code, and accelerate your own software engineering workflow.

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