Looking for a Qdrant expert who can audit our setup and diagnose performance issues
We're running Qdrant in production and want a hands-on review from someone who knows the internals deeply, not just tutorials, but the actual Qdrant docs, config tuning, and real deployment experience.
Specifically looking for help with:
• Diagnosing our current collection/indexing configuration
• Reviewing our vector search query patterns and relevance tuning
• Identifying bottlenecks in our retrieval pipeline
• Advising on payload filtering, HNSW config, and quantization tradeoffs
If you've contributed to Qdrant, answered Qdrant questions on Discord, or built something meaningful with it in production, I'd love to talk.