RAG Architect

About the role

We are seeking an experienced Context Window Optimization / RAG Architect to take full ownership of our enterprise generative AI retrieval performance, accuracy, and operational cost metrics. The ideal candidate will design high-throughput knowledge retrieval systems, optimize semantic context parsing, build custom re-ranking pipelines, and engineer caching grids to deliver data-grounded AI responses with minimal latency and maximum token efficiency.

What you'll do

• Architect end-to-end advanced Retrieval-Augmented Generation (RAG) pipelines , building structures for document parsing, semantic metadata enrichment
• Optimize context window utilization patterns , designing smart parent-child chunking models, sentence-window retrievals
• Build high-performance re-ranking layers , deploying machine learning cross-encoders (e.g., Cohere Rerank
• Implement automated semantic caching architectures , utilizing caching layers (e.g., GPTCache) to capture recurring semantic queries
• Establish automated data chunking pipelines , configuring ingestion routines to cleanly parse semi-structured and unstructured formats (PDFs, corporate wikis

What you need

• 6–10 yrs of experience
• SQL
• Python
• AWS
• LLMs
• Data structures
• GCP
• Any graduate

Good to have

• Azure
• Machine learning
• Databricks