Role RAG AI Architect Remote
Seeking an experienced RAG Architect to design and lead scalable Retrieval-Augmented Generation (RAG) solutions using enterprise data, LLMs, vector search, and AI orchestration technologies.
Key Responsibilities
Design end-to-end RAG architecture for enterprise AI applications.
Architect document ingestion, chunking, embedding, indexing, retrieval, and generation pipelines.
Design and optimize vector search and semantic retrieval solutions.
Integrate LLMs, embedding models, vector databases, and enterprise data sources.
Implement advanced retrieval techniques including hybrid search, reranking, metadata filtering, and query optimization.
Design RAG solutions using frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
Establish RAG evaluation frameworks for relevance, accuracy, groundedness, hallucination, and retrieval quality.
Implement security, access control, PII protection, guardrails, and responsible AI practices.
Design scalable APIs and microservices for production RAG applications.
Collaborate with Data Engineering, ML Engineering, Cloud, Security, and Application teams.
Lead technical design, architecture reviews, POCs, and production implementation.
software/AI engineering experience with strong architecture experience.
Strong hands-on experience with RAG and LLM-based applications.
Expertise in Python, LLMs, embeddings, prompt engineering, and NLP.
Strong knowledge of Vector Databases such as Pinecone, Weaviate, Milvus, pgvector, or OpenSearch.
Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
Strong understanding of semantic search, hybrid search, reranking, chunking, embeddings, and retrieval optimization.
Experience with AWS, Azure, or Google Cloud Platform AI/cloud services.
