RC
RAG Architect
Reuben Cooley, Inc.
🌏 Worldwide
Remote
Staff / Principal
5 days ago
- RAG
- Vector Search
- AI
- LangChain
- LangGraph
- LlamaIndex
- Microservices
- Python
- Natural Language Processing
- Pinecone
- Weaviate
- Milvus
- pgvector
- OpenSearch
- AWS
- Azure
- GCP
- REST API
- Docker
- Kubernetes
- CI/CD
- MLOps
- RBAC
- AWS Bedrock
- Azure OpenAI
- Vertex AI
- Machine Learning
5 days ago
Job Summary
We are 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.
Required Skills
- 8+ years of 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 GCP AI/cloud services.
- Experience designing REST APIs, microservices, and scalable AI platforms.
- Knowledge of Docker, Kubernetes, CI/CD, and MLOps.
- Strong understanding of AI security, data privacy, RBAC, and LLM guardrails.
Preferred Skills
- Experience with Agentic AI / AI Agents.
- Knowledge of Graph RAG / Knowledge Graphs.
- Experience with multimodal RAG.
- Experience with AWS Bedrock, Azure OpenAI, or Google Vertex AI.
- Experience with RAG evaluation and observability platforms.
- Experience building enterprise-grade GenAI platforms.
RAG Architect · Reuben Cooley, Inc.