Responsibilities: Design and implement a scalable RAG system for real-time Q&A across internal content (meetings, messages, documents, whiteboards, videos, etc.); Build robust ingestion and indexing pipelines for semi-structured data sources with ne-grained, permission-aware access control; Develop APIs and backend systems to enable efficient querying, retrieval, and ranking; Collaborate with ML/NLP engineers to iterate on embedding models and improve search quality; Ensure reliability, low latency, and scalability across the entire data retrieval and augmentation stack; Monitor system performance and optimize for high-throughput, low-latency workloads under real-world load. Must have 2 years of experience in the following: 2 years of experience with Backend or distributed systems engineering; 2 years of experience Building systems with Cassandra, ScyllaDB, Postgres, ElasticSearch, Redis, Kafka, and RabbitMQ; 2 years of experience with Cloud-native tools including Docker, Kubernetes, and AWS; and.