Qdrant just closed a $50M Series B to build the retrieval infrastructure layer for modern AI. Our vector search engine, written in Rust, runs in production at engineering teams inside Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom. We're growing our European field team and looking for a Solutions Engineer who wants to get into the room (or the call) where real AI infrastructure decisions get made.

This is a key role on a technical field team. You'll work closely with senior engineers and account executives, learn how enterprise AI systems are designed and sold, and own technical engagements with customers.

Tasks

• Support technical evaluations and proof-of-concept builds alongside senior Solutions Engineers and Account Executives, helping prospects test Qdrant against their real workloads.
• Build demo environments, sample integrations, and reference architectures that showcase Qdrant's performance advantages in RAG pipelines, semantic search, and agentic systems.
• Respond to technical questions from prospective customers, translating infrastructure requirements into working solutions.
• Learn the competitive landscape across vector search and AI infrastructure, and contribute to battlecards, technical guides, and internal knowledge bases.
• Represent Qdrant in online communities, developer forums, and European tech events as you build your own technical presence.
• Work directly with our product and engineering teams to surface customer feedback that shapes the roadmap.

Requirements

What We're Looking For

• 1 to 3 years of experience in software engineering, data engineering, solutions engineering, or a closely related technical role
• Comfortable writing and reading code (Python required; experience with other languages is a plus)
• Genuine curiosity about AI infrastructure: you've built something with LLMs, embeddings, or vector search, even if just as a side project
• Strong communicator in English, written and verbal. You can explain a technical tradeoff clearly to someone who wasn't in the room when it was made
• Self-directed: you're comfortable figuring things out, asking good questions, and closing your own knowledge gaps without waiting to be told
• Based in Europe and available to work within European business hours

Nice to Have

• Exposure to vector search, approximate nearest neighbor algorithms, or semantic retrieval
• Experience with data pipelines, cloud infrastructure, or distributed systems
• Familiarity with RAG, agentic AI frameworks (LangChain, LlamaIndex, CrewAI), or embedding models
• Background in a customer-facing technical role (support engineering, developer advocacy, technical writing)

Benefits

• Equity with meaningful upside in a well-capitalized, fast-scaling company
• Hardware budget: choose your own setup
• Flexible hours, async-friendly culture, and regular team offsites