Platform Engineer

Requirements:

• Vector Database Engines: Deep expertise in specialized vector databases (e. g., Vespa, Qdrant, Milvus) and familiarity with vector extensions in traditional DBs (e. g., pgvector).
• Distributed Systems: Strong foundation in distributed indexing, sharding strategies, and consistency models (e. g., Raft consensus, Paxos).
• Infrastructure: Kubernetes (AKS/GKE), Helm, and Terraform for large-scale, automated infrastructure provisioning and infra scaling automation, etc.
• Performance Engineering: Experience with low-latency search optimizations, quantization (product quantization, HNSW indexing, and BM25+ vector search optimization), and caching strategies.
• Monitoring and Observability: Prometheus, Grafana, Jaeger/Tempo (distributed tracing), and custom automation for health checks.
• Languages: Go/Rust (for performance-critical core platform components) and Python (for client SDKs and DS integration).
• ML Lifecycle Integration: Make vector databases available for various use cases at scale, like similarity search, vector search, RAG systems, etc.