AI Engineer

We're building Europe's leading platform for managing warehouse, logistics, retail, and multi-site commercial real estate portfolios. Our unified facilities management platform powers daily operations of over 250 million m² of real estate worldwide, supporting more than 500,000 professionals across 100,000+ buildings in 35+ countries. We're backed by a leading growth equity investor, scaling rapidly through strategic acquisitions and organic growth, and partnering with some of the world's largest real estate operators. We are now investing in AI across the product, and this role is the first dedicated AI engineer on the team.

• 1+ year of commercial experience delivering AI or LLM features in production environments.
• 5+ years of programming experience with a solid engineering foundation.
• Understanding of distributed systems and writing maintainable code.
• Experience with REST API design and integration patterns in distributed SaaS architectures.
• Ability to evaluate trade-offs across cost, latency, quality, and operational complexity.
• Professional English proficiency for cross-functional collaboration.

AI integration design and delivery:

• Design and implement AI integrations within the existing SaaS product ensuring reliability, scalability, and production readiness.
• Build LLM-based feature pipelines and integration patterns for other engineers to build on.

Guardrails and observability:

• Build guardrail and observability systems to ensure LLM outputs are safe, consistent, and auditable, including prompt validation, output filtering, and monitoring via tools like Langfuse.

MCP integration:

• Lead Model Context Protocol (MCP) integration across the architecture in a Server-Client model, collaborating with platform and backend engineers.

Model evaluation and selection:

• Evaluate and select AI models with structured analysis across inference cost, latency, accuracy, and vendor trade-offs.
• Own the decision and document the reasoning.

Evaluation frameworks:

• Define and implement evaluation frameworks for testing AI features covering correctness, performance, regression, and business alignment.

LLM infrastructure:

• Maintain and iterate on LLM infrastructure including key management, multi-vendor proxying, and model lifecycle management.

Architecture documentation and knowledge sharing:

• Document AI architecture decisions and contribute to engineering-wide knowledge sharing, laying foundations for others.

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