RAG Architect

• 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

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.

• 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 ( Llama Index , Lang Chain ), 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).

• 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.