RAG Developers for Scalable Knowledge Systems

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We help remote-first startups hire RAG developers who design and implement retrieval-augmented generation systems using real-world data pipelines. These engineers understand embeddings, vector databases, search optimization, and context orchestration.

From proof of concept to production-grade knowledge systems.

Retrieval Architecture Design

Designing end-to-end RAG pipelines including embedding generation, chunking strategies, hybrid search, and reranking.

Vector Database Expertise

Hands-on experience with Pinecone, Weaviate, Milvus, FAISS, or OpenSearch for scalable semantic retrieval.

Context Engineering

Optimizing prompt construction, document structuring, and token efficiency to improve answer relevance and reduce hallucination.
How We Assess RAG Developers

Every RAG engineer is evaluated across practical production dimensions:

  • Real-world data ingestion and preprocessing
  • Embedding strategy and chunk optimization
  • Retrieval performance tuning and latency control
  • Evaluation metrics and hallucination reduction
  • Deployment within SaaS or internal AI products
Why This Role Matters

Strong RAG systems require more than connecting a vector database to an LLM. They demand engineers who understand information retrieval, system reliability, and model behavior under production load.

We prioritize implementation depth, not surface-level experimentation.