Graph / RAG Engineer - Remote

Graph / RAG Engineer - Remote

 

Experience: 7+ years, with production experience building graph and retrieval systems at scale.

You own the knowledge layer: existing homegrown code-graph generator and RAG toolchain, and the evidence-pack assembler that feeds the reasoning tier. The guiding principle of the whole design is that the model never goes hunting — you are the reason that holds true.

Responsibilities

Assess the homegrown code-graph generator — coverage, resolution percentage, correctness — and recommend build-versus-leverage; then improve it.

Own the evidence-pack assembler: deterministically gather, per item, the complete bounded context (structure, reference graph, access pattern, liveness, target-side match, business-rule linkage) so the reasoning tier never has to retrieve for itself.

Design, scale and operate the code/knowledge graph (Neo4j or Amazon Neptune): ingestion pipelines, cross-graph correlation across schema, repositories and batch, and impact-simulation queries.

Tune the retrieval pipeline — embeddings strategy, hybrid dense/sparse retrieval, and grounding-quality measurement.

Harden the RAG toolchain for production on AWS: high availability, backup and recovery, and cost-efficiency at scale.

Build the natural-language query surface over the graph ("what breaks if we retire table X").

Qualifications

Python — production-grade.

Graph databases — Neo4j and/or Amazon Neptune; Cypher / Gremlin; graph modelling and query optimisation at scale.

Code-graph and static-analysis construction: ASTs, call graphs, cross-repository symbol resolution — across multiple language stacks.

Production RAG: vector stores, embeddings strategy, hybrid retrieval, grounding evaluation.

AWS — scaling, high availability, cost-efficient operation.

Comfortable working across seven to eight different language and tech stacks.

Working knowledge

LangGraph / LangChain; ontology and semantic-layer design.

SQL Server; Informatica metadata models; CI/CD automation.