Solutions Architect - Langfuse

What You'll Be Doing

Pre-Sales & Technical Advisory

• Lead technical evaluations with AI engineering teams considering ClickHouse 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 ClickHouse | Langfuse 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 ClickHouse AEs to progress and close opportunities within the AI and LLM observability segment
• Advocate internally for product improvements and integration enhancements that strengthen the ClickHouse + Langfuse story

Ecosystem & Community Presence

• Serve as ClickHouse's primary technical voice in the Langfuse community — contributing to forums, engaging on GitHub, 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 ClickHouse| Langfuse as the analytics backbone for LLM observability workloads

What You Bring

• 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 architectures, evaluation frameworks, token economics, and the data infrastructure that supports them
• Customer-facing experience — pre-sales, solutions engineering, developer advocacy, or technical account management. You've navigated technical conversations with real stakes and know how to build trust with engineering teams
• Strong foundation in data infrastructure — experience with analytical databases, distributed systems, and cloud infrastructure. Familiarity with ClickHouse, Postgres, or columnar databases is a strong plus
• Open source orientation — you understand how open source communities work, how developer trust is earned, and how to contribute authentically rather than just promote