AI Engineer - Graphrag Specialist

Opus Recruitment Solutions; City Of London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

A leading recruitment firm is seeking an experienced AI Engineer to design and implement Graph-based Retrieval-Augmented Generation pipelines for enterprise-scale AI solutions. The role requires strong expertise in graph-based architectures, Python proficiency, and hands-on experience with graph databases. This hybrid position offers an opportunity to work on cutting-edge projects in knowledge graph construction and multi-hop reasoning.

Qualifications

• Strong experience with GraphRAG or Graph-based RAG architectures.
• Proficiency in Python and frameworks like LangChain, Semantic Kernel, or similar.
• Hands-on with graph databases and vector DBs.
• Knowledge of LLMs, prompt engineering, and retrieval optimization.
• Familiarity with knowledge graph construction and graph algorithms.

Responsibilities

• Build and optimize GraphRAG pipelines integrating LLMs with knowledge graphs.
• Develop graph-based retrieval strategies combining with vector search.
• Implement graph traversal algorithms and entity-relation extraction.
• Collaborate with data scientists and engineers for scalable AI solutions.

Skills

GraphRAG or Graph-based RAG architectures

Python

Graph databases (Neo4j, TigerGraph)

Vector databases (Pinecone, Weaviate, FAISS)

Knowledge of LLMs

Prompt engineering

Retrieval optimization

Knowledge graph construction

Entity linking

Graph algorithms

Job description

Location: Hybrid (London preferred)
Contract: 6-12 months (Outside IR35)
Rate: £500-£700/day (DOE)

Looking for an experienced AI Engineer to design and implement Graph-based Retrieval-Augmented Generation (GraphRAG) pipelines for enterprise-scale AI solutions. This is a hands-on role working on cutting-edge projects in knowledge graph construction, multi-hop reasoning, and hybrid retrieval systems.

Key Responsibilities

• Build and optimize GraphRAG pipelines integrating LLMs with knowledge graphs.
• Develop graph-based retrieval strategies and combine them with vector search for hybrid RAG.
• Implement graph traversal algorithms and entity-relation extraction from unstructured data.
• Collaborate with data scientists and engineers to deploy scalable AI solutions.

Required Skills

• Strong experience with GraphRAG or Graph-based RAG architectures.
• Proficiency in Python and frameworks like LangChain, Semantic Kernel, or similar.
• Hands-on with graph databases (Neo4j, TigerGraph) and vector DBs (Pinecone, Weaviate, FAISS).
• Knowledge of LLMs, prompt engineering, and retrieval optimization.
• Familiarity with knowledge graph construction, entity linking, and graph algorithms.

Must have full right to work and live in the UK to be considered for this role