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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Customer Support Engineer, AI Infrastructure & Observability - EMEA - **Company:** ClickHouse - **Location:** Paris, France (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Engineering, Databases, Software Debugging, Distributed Systems, Python (Programming Language), Online Analytical Processing, Open Source Technology, RabbitMQ, Next.js, Software Engineering, SQL Databases, TypeScript, AI Infrastructure, Large Language Models, Apache Spark, Apache Kafka, Vertica, Data Pipelines - **Published:** August 26, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=cc37817365487e09 ## About the Role * Databases & OLAP Systems: Hands-on experience with databases, OLAP, or distributed systems (ClickHouse, SQL engines, or cloud-native SaaS infrastructure). * Systems Fundamentals: Strong software engineering fundamentals (CS, Data Science degree, or comparable hands-on project experience) with comfort reading code, working with data pipelines (e.g., Kafka, Spark), or debugging cloud infrastructure (AWS/GCP/Azure). * Distributed & Operational Rigor: Comfort working in a global, remote-first team aligned to SLA coverage, on-call needs, and async communication. For Langfuse & AI Observability: * LLM Tooling & Application Dev: Genuine curiosity for LLM application development with hands-on experience building, debugging, or evaluating LLM-powered products using frameworks like Python, TypeScript, LangChain, LlamaIndex, or Vercel AI SDK. * Tracing, Evals & Developer Tooling: Familiarity with LLM observability/eval tooling, prompt workflows, model provider APIs (OpenAI, Anthropic), or OpenTelemetry-style tracing to unblock AI-product teams. * Technical Support & Communication: Strong written communication across registers. You have concise Slack troubleshooting, clear long-form docs/guides, and a customer-first mindset to unblock engineers. Bonus Points * Experience with OSS and open-source technologies, as a user, community member, or contributor * Experience with ClickHouse * Experience with Langfuse specifically, or with LLM observability/eval tooling more broadly. * Experience with AWS, GCP, or Azure. * Experience with data pipeline technologies such as Kafka, Kinesis, Spark, or RabbitMQ. ## Description * Supporting and guiding our ClickHouse & Langfuse users, customers, and prospects via cases, chat, Slack, community, and phone calls, delivering high-touch guidance within required Service Level Agreements (SLAs) as part of a global on-call coverage rotation. * Develop solutions based on ClickHouse Cloud and ClickHouse open-source that can be shared with our users, community, and customers via documentation, knowledge base, blogs, meetups, webinars, and training. * Work closely with our global Support Services, Engineering, Go-to-Market, and Product Management teams to help define the functionality required by users and customers. * Assist with mentoring, training, and sharing your knowledge with colleagues, users, and customers. * Escalate bugs to product engineering and close the loop with the customer once shipped. * Go through deep technical integration/implementation questions together with our customers and directly within their codebases. * Build strong, trusted relationships with colleagues, customers, and partners across the developer ecosystem ## Related Videos - [Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse) - [Beyond Kafka & RabbitMQ: Why NATS is the Future of Microservices Messaging](https://www.wearedevelopers.com/videos/1646-beyond-kafka-rabbitmq-why-nats-is-the-future-of-microservices-messaging) - [GraphQL + Apollo + Next.js: A Lovely Trio](https://www.wearedevelopers.com/videos/311-graphql-apollo-next-js-a-lovely-trio) - [Why LLMs Need Observability and How to Do It](https://www.wearedevelopers.com/videos/2117-why-llms-need-observability-and-how-to-do-it) - [Why Systems Break After Initial Success: The Architectural Failures That Take Months to Surface](https://www.wearedevelopers.com/videos/2048-why-systems-break-after-initial-success-the-architectural-failures-that-take-months-to-surface) - [From Zero to Hero: NextJS 13 and Tailwind CSS for Beginners](https://www.wearedevelopers.com/videos/766-from-zero-to-hero-nextjs-13-and-tailwind-css-for-beginners) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)