Solutions Architect - Langfuse
AI summary of the role
This is a dedicated Solutions Architect role for Langfuse, ClickHouse's recently acquired LLM observability platform.
What you’ll do
- Lead technical evaluations with AI engineering teams considering ClickHouse as their observability data store, from architecture review through POC and production deployment
- Source and qualify pipeline directly through ecosystem relationships and community engagement, opening doors in the LLM observability segment
- Serve as ClickHouse's primary technical voice in the Langfuse community, contributing to forums, GitHub, and events
- Design and deliver reference implementations, schema designs, and ingestion patterns optimized for LLM trace data at scale
What you’ll bring
- Hands-on experience in the LLM observability or AI monitoring space, either as a vendor or practitioner
- Technical depth in the modern AI stack, including prompt engineering, RAG architectures, evaluation frameworks, and token economics
- Customer-facing experience in pre-sales, solutions engineering, developer advocacy, or technical account management
- Strong foundation in data infrastructure, including analytical databases, distributed systems, and cloud infrastructure
Technologies
ClickHouse · Langfuse · LLM observability · RAG · prompt engineering · evaluation frameworks · token economics · Postgres · columnar databases · GitHub
About ClickHouse
Open-source columnar OLAP database and managed cloud for real-time analytics, observability, and AI workloads at petabyte scale.
Series D · 200–500 people
Source and classification
Technical pre-sales · Evidence for this classification:
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. 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
More from the job description
About the Role 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. What You'll Be Doing Pre [... source excerpt omitted ...] 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 [... source excerpt omitted ...] orkloads 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 data
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