An early-career Associate Data Engineer role focused on architecting RAG pipelines, scaling vector infrastructure, and building semantic layers for AI-driven infrastructure.
Compensation
The compensation for this position is $77,000 based on a full-time schedule.
Responsibilities
Architect for RAG: design and scale pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings.
Scale vector infrastructure: ensure health and performance of vector databases (e.g., Pinecone, Milvus, or Weaviate).
Engineer semantic layers: build knowledge graphs and semantic layers for AI context.
Automate data excellence: build automated data guardrails to detect noise, bias, or PII.
Solve meaningful challenges: bridge between raw data sources and deep technical AI work.
An early-career data engineering role focused on architecting RAG pipelines, managing vector infrastructure, engineering semantic layers, and building CI/CD pipelines for data infrastructure.
Compensation
The compensation for this position is $77,000 based on a full-time schedule.
Responsibilities
Architect for RAG: design and scale the pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings.
Scale vector infrastructure: responsible for the health and performance of vector databases (e.g., Pinecone, Milvus, or Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops.
Engineer semantic layers: build knowledge graphs and semantic layers that provide agents with the necessary context to navigate complex infrastructure puzzles.
Automate data excellence: build automated data guardrails to detect noise, bias, or PII (Personally Identifiable Information) before it reaches the model.
Solve meaningful challenges: serve as the bridge between raw, messy data sources and deep technical AI work, identifying and resolving quality issues at the source.