At a Glance
- Tasks: Lead technical evaluations and engage with AI engineering teams on LLM observability.
- Company: Join ClickHouse, a leader in real-time analytics and AI observability.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with excellent networking opportunities in the AI ecosystem.
- Why this job: Be at the forefront of AI innovation and make a real impact in the tech community.
- Qualifications: Experience in AI monitoring, strong technical skills, and customer-facing expertise.
The predicted salary is between 80000 - 100000 € per year.
Requirements
- 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 — 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; capable of navigating technical conversations with real stakes and building 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 — understanding how open source communities work, how developer trust is earned, and how to contribute authentically rather than just promote.
What the job involves
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.
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 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 application 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 as the analytics backbone for LLM observability workloads.
Solutions Architect (LangFuse EMEA) employer: Deepstreamtech
At ClickHouse, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Solutions Architect for LangFuse in the EMEA region, you will have the opportunity to work at the forefront of AI observability, engaging with cutting-edge technology while building meaningful relationships within the developer community. We offer robust employee growth opportunities, a commitment to open-source values, and a dynamic environment that encourages creativity and technical excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Solutions Architect (LangFuse EMEA)
✨Tip Number 1
Get involved in the community! Join forums, attend meetups, and engage with others in the AI observability space. This not only helps you build connections but also shows your genuine interest in the field.
✨Tip Number 2
Showcase your expertise! Create content like blog posts or tutorials that highlight your knowledge of LLM applications and the ClickHouse + Langfuse stack. This can help you stand out and demonstrate your value to potential employers.
✨Tip Number 3
Practice your technical conversations. Be ready to discuss prompt engineering, evaluation frameworks, and data infrastructure with confidence. The more comfortable you are, the better you'll connect with engineering teams during interviews.
✨Tip Number 4
Apply through our website! We love seeing candidates who take the initiative to reach out directly. It shows you're serious about joining our team and makes it easier for us to spot your application.
We think you need these skills to ace Solutions Architect (LangFuse EMEA)
Some tips for your application 🫡
Show Your Technical Depth:When you're writing your application, make sure to highlight your hands-on experience with LLM observability and the modern AI stack. We want to see that you can comfortably discuss topics like prompt engineering and data infrastructure, so don’t hold back!
Customer-Facing Experience Matters:Don’t forget to mention any customer-facing roles you've had, whether in pre-sales or technical account management. We’re looking for someone who can build trust with engineering teams, so share examples of how you've navigated technical conversations successfully.
Emphasise Open Source Involvement:If you’ve contributed to open source projects or understand how these communities work, let us know! We value authentic contributions over mere promotion, so share your experiences and how they’ve shaped your approach to building trust.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensure it gets the attention it deserves. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Deepstreamtech
✨Know Your Tech Inside Out
Make sure you’re well-versed in the modern AI stack, especially around LLM observability. Brush up on prompt engineering, RAG architectures, and token economics. Being able to discuss these topics confidently will show that you’re not just familiar with the concepts but can also apply them in real-world scenarios.
✨Showcase Your Customer-Facing Experience
Prepare examples from your past roles where you’ve navigated technical conversations with clients or engineering teams. Highlight how you built trust and helped them solve problems. This is crucial as the role involves engaging with various stakeholders, so demonstrating your ability to connect with them is key.
✨Engage with the Community
Familiarise yourself with the Langfuse community and open-source contributions. Be ready to discuss how you’ve engaged with developer communities in the past and how you plan to contribute authentically. This shows that you understand the importance of community in the tech space and are willing to be an active participant.
✨Prepare for Technical Evaluations
Anticipate questions about how you would lead technical evaluations with AI engineering teams. Think through potential architecture reviews and POC strategies you might suggest. Being prepared to discuss specific scenarios will demonstrate your readiness to take on the responsibilities of the role.
