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Qdrant Tech Stack
Open-source vector search engine for AI-powered matching and retrieval
Qdrant is a vector search engine built in Rust, enabling embeddings-based applications for semantic matching, recommendation, and retrieval-augmented generation (RAG). The engineering-heavy hiring mix—16 roles across backend, platform, and cloud infrastructure—paired with active gRPC migration and Kubernetes operator work, signals a shift toward distributed, cloud-native deployment. Internal pain points around operational load and architectural evolution indicate scaling challenges typical of open-source infrastructure projects growing into commercial platforms.
Tech Stack 47 technologies
Core StackReact TypeScript Python RAG Mixpanel Amplitude Kubernetes AWS Rust Neo4j GitHub Go Prometheus Grafana OpenTelemetry TanStack OpenAPI HTML CSS Statsig GCP Azure LlamaIndex CrewAI Discord X Google DeepMind Mistral AI LinkedIn Bash+11 more
What Qdrant Is Building
◆Challenges
- Architectural evolution
- Reducing operational load
- Reducing complexity
- Improving developer productivity
- Delivery predictability
- Event-driven pipeline generation
- Modernising legacy systems
- Reducing friction across company
- Streamlining operations
- Improving hiring funnels
▲Active Projects
- Develop and maintain open-source showcase applications
- Prototype new qdrant use cases via emerging ai patterns
- Lead qdrant’s presence at hackathons and workshops
- Architectural reviews for search and rag solutions
- Kubernetes operators
- Internal tooling for support workflows and observability
- Platform refactors
- Eventing/pub-sub foundations
- Grpc migration
- Frontend architecture for qdrant cloud
Hiring Activity
35 roles · 15 in 30d
Department
Seniority
Notable leadership hires: Chief of Staff
Company intelligence
