Remote Solutions Architect - LangFuse EMEA

About Click House

Recognized on the 2025 Forbes Cloud 100 list, Click House is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, Click House leads the market in real-time analytics, data warehousing, observability, and AI workloads.

The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla.

We’re on a mission to transform how companies use data. Come be a part of our journey!

About the Role

AI applications are being built faster than teams can monitor, debug, or trust them. Click House recently acquired Langfuse — the leading open source LLM observability platform — making it a core part of the Click House product stack. Together, Click House and Langfuse offer engineering teams the most powerful combination in the market: real-time, high-performance analytics infrastructure paired with best-in-class LLM tracing, evaluation, and observability tooling.

This role sits at the center of that combined story.

We're looking for a Langfuse Solutions Architect who is already embedded in the AI observability ecosystem — someone who understands how engineering teams instrument and evaluate LLM applications, and can credibly represent the full Click House + Langfuse platform to the teams that need it most.

This is not a generalist SA role. You'll be our dedicated technical presence in the LLM observability space — opening doors through the Langfuse community, deepening relationships with AI engineering teams, and helping them get the most out of a platform that now spans from raw data infrastructure to production LLM monitoring. You'll work at the intersection of community, pre-sales, and technical advisory, and you'll be the person who makes the Click House + Langfuse stack the obvious choice for teams building serious AI applications.

Pre-Sales & Technical Advisory

• Lead technical evaluations with AI engineering teams considering Click House as their observability data store, from initial architecture review through POC and production deployment
• Engage directly with data engineers, ML engineers, and platform architects to understand their LLM application stack, trace volumes, evaluation workflows, and query patterns — and map those requirements to Click House capabilities
• Work across all levels of customer organizations, from individual contributors building LLM pipelines to CTOs making infrastructure decisions
• Design and deliver reference implementations, schema designs, and ingestion patterns optimized for LLM trace data at scale

Pipeline & Revenue Contribution

• Source and qualify pipeline directly through ecosystem relationships and community engagement — this role is expected to open doors, not just walk through them
• Partner with Click House AEs to progress and close opportunities within the AI and LLM application segment
• Advocate internally for product improvements and integration enhancements that strengthen the Click House + Langfuse story

Ecosystem & Community Presence

• Serve as Click House's primary technical voice in the Langfuse community — contributing to forums, engaging on Git Hub, participating in events, and building authentic credibility with AI engineers and developers
• Develop relationships with the Langfuse core team and ecosystem partners to identify joint GTM opportunities and integration improvements
• Create technical content — blog posts, tutorials, reference architectures, and demo environments — that showcases Click House as the analytics backbone for LLM observability workloads

• Hands-on experience in the LLM observability or AI monitoring space — whether at a vendor or as a practitioner building and operating LLM applications in production
• Technical depth in the modern AI stack — you're comfortable discussing prompt engineering, RAG…