Key skills
PythonJavaKotlinAIGenAILLMClaudeGeminiLlamaRAGLangChainLlamaIndexAutoGenLangGraphPineconeWeaviateKafkaGraphQLPlaywrightAWSAzureGCPKubernetesDockerKinesisNeo4jCI/CD
About this role
Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. They are seeking a talented Senior Gen AI Engineer to design and operate AI agents that traverse and enrich large-scale knowledge graphs, utilizing live data sources for dynamic context extension.
Responsibilities:
- Design, build and maintain large-scale property graphs and RDF triplestores (Neo4j, Amazon Neptune, Stardog, or equivalent)
- Develop and govern ontologies, taxonomies, and entity-relationship schemas that reflect real-world domain semantics
- Implement graph ingestion pipelines that extract, transform, and link entities from structured, semi-structured, and unstructured data
- Optimise graph traversal queries (Cypher, SPARQL, Gremlin) for sub-second response at production scale
- Train and deploy graph neural networks (GNNs) for node classification, link prediction, and subgraph retrieval - Maintain model retraining workflows triggered by graph drift or coverage degradation
- Architect and implement autonomous agents that plan multi-step reasoning chains over knowledge graph data using LLMs (GPT-4o, Claude, Gemini, or open-source equivalents)
- Build graph-aware Retrieval-Augmented Generation (RAG) pipelines that blend structured graph context with unstructured document retrieval
- Design tool-use and function-calling layers so agents can query live data sources — web search, REST/GraphQL APIs, relational databases — to extend or verify graph knowledge
- Implement agent memory, reflection, and self-correction loops to improve reliability over multi-hop tasks
- Integrate web scraping, news feeds, and open-source intelligence (OSINT) sources to keep the knowledge graph current
- Build entity resolution and deduplication components that merge data from heterogeneous sources into a consistent graph
- Develop confidence-scoring and provenance-tracking mechanisms so downstream consumers understand the reliability of any piece of context
- Package agents as scalable microservices; instruments with observability tooling (tracing, latency, token cost)
- Collaborate with platform engineers to deploy workloads on cloud-native infrastructure (AWS / GCP / Azure)
- Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time
Requirements:
- 5 + years of professional software engineering with strong Python (or Java / Kotlin) proficiency
- Hands-on production experience with at least one major graph database — Neo4j, Amazon Neptune, TigerGraph, or comparable
- Demonstrated knowledge of graph query languages like Cypher, SPARQL, or Gremlin — at production query complexity
- Direct experience building LLM-powered agents or pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel
- Solid understanding of RAG architectures: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, and re-ranking
- Familiarity with prompt engineering, few-shot learning, and LLM evaluation techniques
- Experience integrating external data sources via APIs, web scraping (Playwright / Scrapy), or streaming pipelines (Kafka / Kinesis)
- Working knowledge of containerisation (Docker, Kubernetes) and CI/CD pipelines
- Familiarity with graph export formats - at least one GraphML, RDF/OWL, or JSON-LD
- Experience integrating GNN-derived features into vector stores or RAG pipelines
- Advanced degree (MS / PhD) in Computer Science, Information Science, Computational Linguistics, or a related field
- Experience in intelligence, defence, or trade-craft environments — working with OSINT, link analysis, entity disambiguation, or signals intelligence data
- Understanding of access-control models for sensitive graph data (need-to-know, compartmentalisation, provenance labelling)
- Familiarity with knowledge representation standards like OWL, SHACL, RDF-star, JSON-LD, W3C PROV
- Experience with fine-tuning or instruction-tuning open-source LLMs (Llama, Mistral, Falcon) for domain-specific tasks
- Background in network-analysis algorithms: centrality, community detection, path-finding, anomaly detection on graphs
- Contributions to open-source graph or GenAI projects; published research or technical blog presence
- Active or adjudicatable security clearance (Secret or above) — strongly preferred for trade-craft assignments