Solutions Architect - Langfuse

Навыки

• Распределённые системы
• GitHub
• Google Tag Manager
• Jaeger / OpenTelemetry
• LLM
• Machine Learning
• Мониторинг и observability

О компании и продукте

• AI applications are being built faster than teams can monitor, debug, or trust them. ClickHouse recently acquired Langfuse — the leading open source LLM observability platform — making it a core part of the ClickHouse product stack. Together, ClickHouse 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 ClickHouse + 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 ClickHouse + Langfuse stack the obvious choice for teams building serious AI applications.

Задачи

• 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 | Lanfguse 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

Требования

• 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

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