LLM Observability Solutions Architect

ClickHouse; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Flexible work environment

Healthcare contributions

Equity

Flexible time off

Remote home office allowance

Global gatherings

Job summary

ClickHouse is seeking a dedicated technical advisor for LLM observability and AI monitoring. You’ll lead evaluations with engineering teams, map requirements to ClickHouse Langfuse capabilities, and craft reference implementations that scale LLM trace data.

You will engage with data and platform engineers, collaborate with pre-sales, and contribute to the ecosystem through tutorials and demos. This role emphasizes a deep AI stack, open source collaboration, and scalable analytics.

Qualifications

• Hands-on experience in the LLM observability or AI monitoring space.
• Technical depth in the modern AI stack, including prompt engineering and RAG architectures.
• Customer-facing experience in pre-sales, solutions engineering, or technical account management.
• Strong foundation in data infrastructure and cloud infrastructure; familiarity with ClickHouse or columnar databases.

Responsibilities

• Lead technical evaluations with AI teams from architecture review through POC and production deployment.
• Engage with data/ML engineers and platform architects to map LLM stack requirements to ClickHouse Langfuse capabilities.
• Design and deliver reference implementations, schemas, and ingestion patterns for LLM trace data at scale.
• Build ecosystem relationships and contribute technical content to showcase the ClickHouse + Langfuse stack.

Skills

LLM observability

AI monitoring

prompt engineering

RAG architectures

data infrastructure

ClickHouse

Job description

ClickHouse is seeking a dedicated technical advisor for LLM observability and AI monitoring. You’ll lead evaluations with engineering teams, map requirements to ClickHouse Langfuse capabilities, and craft reference implementations that scale LLM trace data.

You will engage with data and platform engineers, collaborate with pre-sales, and contribute to the ecosystem through tutorials and demos. This role emphasizes a deep AI stack, open source collaboration, and scalable analytics.