Our client, an international AI development company based in New York, is currently seeking a Structured Data RAG Engineer to lead the design and development of Retrieval-Augmented Generation (RAG) systems optimized for structured data sources such as relational databases, APIs, and enterprise data warehouses.

The ideal candidate will have experience using advanced frameworks like GraphRAG and LangGraph to enable reasoning over structured, interconnected datasets. This role bridges data engineering with LLM capabilities, delivering natural language interfaces and intelligent querying systems for enterprise data.

Key Responsibilities

Structured Data Integration:

Develop connectors and pipelines to ingest structured data from SQL/NoSQL databases, APIs, and data lakes

Convert tabular and relational data into vectorized and graph-structured formats for LLM access

RAG System Development:

Build structured data RAG systems using GraphRAG and LangGraph to enable multi-hop reasoning and context-aware retrieval

Integrate vector databases (e.g., FAISS, Milvus, Weaviate) for scalable retrieval over structured corpora

LLM Alignment & Prompt Engineering:

Design prompt templates tailored to structured queries and logic-driven reasoning

Enhance accuracy and interpretability of LLM outputs in structured data environments

Model Evaluation & Optimization:

Write custom evaluation code and design benchmarking frameworks

Measure response accuracy, relevance, latency, and scalability across large data sets

Deployment & Automation:

Leverage cloud infrastructure (GCP, AWS, or Azure) and implement CI/CD pipelines

Collaborate with ML, data, and backend engineering teams to productionize RAG systems

Qualifications & Skills

Expertise in Retrieval-Augmented Generation (RAG), including structured/tabular data use cases

Strong proficiency with GraphRAG approaches and multi-agent development using LangGraph

Experience with graph databases, including LightRAG or Microsoft Graph RAG

Solid background in SQL, Redshift, relational databases, and structured APIs

Proficient in Python, LangChain, vector stores, and agentic orchestration

Familiarity with LLMs, prompt engineering, and structured context integration

Strong analytical mindset and attention to detail

Collaborative and communicative across cross-functional teams

Proactive, innovative thinker with a product-oriented approach to AI applications

Very strong English communication skills, both written and verbal (essential for global collaboration)

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