Job Description
KEY RESPONSIBILITIES Knowledge Graph Engineering 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. Agentic AI Systems 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. Context Enrichment & Data Fusion 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. MLOps & Production Readiness 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 / Google Cloud Platform / Azure). Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time. REQUIRED SKILLS & EXPERIENCE 7-10 years of professional software engineering with strong Python (or Java / Kotlin) proficiency. 2+ Yrs, 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 PREFERRED QUALIFICATIONS 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.