Opportunity Description
Responsibilities
- Architect for RAG: Design and scale pipelines for Retrieval-Augmented Generation (RAG), transforming large volumes of unstructured IT logs and documentation into optimized vector embeddings.
- Scale vector infrastructure: Oversee the health and performance of vector databases (Pinecone, Milvus, Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops.
- Engineer semantic layers: Build knowledge graphs and semantic layers beyond simple ETL to provide agents with the necessary context for navigating complex infrastructure puzzles.
- Automate data excellence: Build automated guardrails to detect noise, bias, or PII before it reaches the model.
- Bridge raw, messy data sources and deep technical AI work, identifying and resolving quality issues at the source.
- Progress to production: Build, deploy, and maintain CI/CD pipelines for data infrastructure, ensuring that the context window re...
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