GraphRAG Engineer

Role Description

We are seeking a skilled Graph RAG Engineer to design, build, and operationalize a Graph-based Retrieval-Augmented Generation (GraphRAG) system. This role sits at the intersection of knowledge graph engineering, data pipeline development, and applied AI. The ideal candidate will develop Python-based automation to discover and extract relevant features from structured and unstructured data sources, populate a graph database, and build agentic AI workflows on top of that knowledge graph. They will shape how structured knowledge is extracted, organized, and reasoned over at scale, with direct impact on mission-critical analytical products.

• Architect, design, build, and extend GraphRAG solutions that combine knowledge graphs, graph databases, retrieval pipelines, and large language models.
• Design graph schemas, ontologies, taxonomies, metadata models, and graph data models tailored to domain-specific business and analytical use cases.
• Develop Python scripts and modules to automate entity extraction, entity resolution, relationship extraction, feature discovery, metadata capture, tagging, and graph enrichment.
• Build and populate graph databases using extracted concepts, relationships, source evidence, and domain-specific ontologies.
• Create repeatable data ingestion and transformation pipelines for structured, semi-structured, and unstructured data sources.
• Integrate graph databases and graph query results into retrieval-augmented generation workflows and LLM context windows to improve answer accuracy, explainability, and traceability.
• Develop and apply AI agents or agentic frameworks that interact with GraphRAG systems and support multi-step reasoning and query resolution.
• Implement validation, traceability, lineage, and quality checks for generated graph data, retrieval outputs, and graph structure.
• Collaborate with domain experts, data engineers, AI engineers, ML engineers, and solution architects to translate business needs into GraphRAG capabilities and document designs, data flows, graph structures, automation logic, and integration points.

Qualifications

• Graph Database Development
• Demonstrated experience building and populating a property graph database.
• Proficiency in Cypher query language for graph traversal, filtering, and aggregation.
• Ability to design node/relationship schemas and ontologies from scratch.
• GraphRAG Implementation
• Hands-on experience building a GraphRAG system — including graph-aware retrieval, entity linking, and context injection into LLM prompts.
• Understanding of how graph structure improves over flat vector search for multi-hop or relational queries.
• Python Development
• Strong Python skills, including object-oriented design, modular package development, and scripting for data automation.
• Experience with NLP or information extraction libraries such as spaCy, transformers, LangChain, LlamaIndex, or similar for automated feature and entity discovery.
• Agentic AI Frameworks
• Experience developing or extending LLM-based agents using frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, or similar.
• Ability to chain tools, memory, and graph lookups within an agentic loop.
• General
• Strong debugging, documentation, and software engineering practices.
• Ability to work with technical and non-technical stakeholders to understand data meaning and business context.

Requirements

• Experience with graph ML libraries such as PyG (PyTorch Geometric) or NetworkX for graph analytics or embedding generation.
• Familiarity with knowledge graph embedding models (TransE, Node2Vec, GraphSAGE) to enhance retrieval.
• Experience with vector databases (Pinecone, Weaviate, Chroma) used in hybrid graph + vector retrieval architectures.
• Prior work in a government, defense, or intelligence community environment.
• Experience with ETL/ELT pipelines and orchestration tools such as Airflow, Prefect, or dbt.
• Familiarity with Azure OpenAI, AWS Bedrock, or on-premise LLM deployments for air-gapped or controlled environments.
• Understanding of data provenance, traceability, and explainability requirements in regulated or classified settings.
• Experience with Microsoft GraphRAG (open-source) or similar production GraphRAG implementations.

Education

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field — or equivalent demonstrated experience.
• Advanced degree a plus but not required.

Benefits

• Market-competitive salary.
• Generous PTO package.
• Comprehensive medical, dental, vision, and life insurance plans.
• 401K.
• Short/long-term disability insurance.
• A fun and engaging culture.
• Training opportunities to keep you up to speed on the latest technologies.