RAG Architect

Zoho; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Fyerx is seeking an experienced Context Window Optimization / RAG Architect to own enterprise generative AI retrieval performance, accuracy, and cost metrics. You will design high-throughput retrieval systems, optimize semantic parsing, build re-ranking pipelines, and engineer caching grids for data-grounded AI responses with minimal latency and maximum token efficiency.

As the lead, you will architect end-to-end RAG pipelines, optimize chunking and context usage, implement semantic caching, and

Qualifications

• 6 to 10 years of enterprise data engineering, database design, or search engine engineering experience.

Responsibilities

• Architect end-to-end advanced Retrieval-Augmented Generation (RAG) pipelines, with document parsing and multi-vector lookups.
• Optimize context window utilization with smart chunking, sentence-window retrievals, and sliding window strategies.
• Build high-performance re-ranking layers using cross-encoders (e.g., Cohere Rerank, BGE-Reranker).
• Implement automated semantic caching architectures to reduce API token costs and latency.
• Establish automated data chunking pipelines to parse PDFs, wikis, and SQL outputs into vector targets.
• Govern vector similarity spaces by fine-tuning hybrid search combining dense embeddings with BM25.
• Audit context-level hallucination rates and logs to improve precision and recall.

Skills

Python

Embedding models

Context window optimization

Performance engineering

Education

Cloud Data/Database Engineer Certification

Specialty Analytics credential (AWS/GCP/Azure)

Tools

Pinecone

Milvus

Weaviate

LlamaIndex

LangChain

BM25

GPTCache

Job description

• Total Experience Required: 6 to 10 years
  
• Relevant Experience Required: 3+ years of dedicated experience designing production-grade Retrieval-Augmented Generation (RAG) architectures and optimizing LLM token throughput
  

Job Summary

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.

Key Responsibilities

• Architect end-to-end advanced Retrieval-Augmented Generation (RAG) pipelines, building structures for document parsing, semantic metadata enrichment, and multi-vector lookups.
  
• Optimize context window utilization patterns, designing smart parent-child chunking models, sentence-window retrievals, and sliding window strategies to eliminate irrelevant text tokens.
  
• Build high-performance re-ranking layers, deploying machine learning cross-encoders (e.g., Cohere Rerank, BGE-Reranker) to score retrieved documents before feeding them into the LLM context pool.
  
• Implement automated semantic caching architectures, utilizing caching layers (e.g., GPTCache) to capture recurring semantic queries, reducing API token expenditures and response latencies.
  
• Establish automated data chunking pipelines, configuring ingestion routines to cleanly parse semi-structured and unstructured formats (PDFs, corporate wikis, SQL outputs) into clean vector targets.
  
• Govern vector similarity spaces, fine-tuning hybrid search algorithms that cleanly combine dense semantic embeddings with sparse keyword token indexes (BM25).
  
• Audit context-level hallucination rates and accuracy logs, tracking precision metrics, retrieval recall bounds, and processing speeds to systematically eliminate incorrect model generations.
  

Requirements

• 6 to 10 years of enterprise data engineering, database design, or search engine engineering experience, with 3+ dedicated years actively scaling context retrieval loops for live LLM applications.
  
• Strong technical mastery of Python, vector databases (Pinecone, Milvus , Weaviate ), text embedding models, open-source orchestration tools ( LlamaIndex , LangChain ), and SQL.
  
• Deep structural understanding of context window limitations ("lost in the middle" phenomenon), multi-modal token dynamics, network data transfer speeds, and cloud memory spaces.
  
• Mandatory certification: Professional Cloud Data/Database Engineer or Specialty Analytics credential from a major cloud vendor (AWS/GCP/Azure).
  

Preferred Qualifications

• Prior experience implementing Graph RAG frameworks utilizing native knowledge graphs (e.g., Neo4j) to map complex corporate data relationship networks.
  
• Familiarity with fine-tuning open-source text embedding models specifically optimized for industry-specific terminology or legacy product schemas.