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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Knowledge Engineering Senior Analyst - **Company:** Accenture - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Data Architecture, Extract Transform Load (ETL), Relational Databases, Graph Database, Python (Programming Language), Search Algorithms, Machine Learning, Neo4j, Tensorflow, SPARQL, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Apache Nifi, Data Pipelines - **Published:** July 2, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=669a1c7f5df27bef ## About the Role You are a a strong individual contributor knowledge engineer growing into a team lead. You are well versed in the full knowledge graph development lifecycle. You formulate real-world problems into practical, efficient, scalable AI and Knowledge Graph solutions, working hands-on across the lifecycle from ingestion through modeling, curation, and deployment. You apply current methodologies, techniques, and algorithms with the right architecture, and begin to guide junior engineers on the project. You stay current with knowledge engineering, generative AI, LLM, and multi-modal models; look for opportunities to apply them to the problem at hand. You design, evaluate, and maintain ontologies as needed. You help articulate the value of generative AI and knowledge graph approaches for a given business problem. You share what they learn with the team. You collaborate with users, use case reps, engineers, architects, and UI designers to deliver their piece of an end-to-end solution., * Bachelor's degree or equivalent, plus at least 3 of the following: * Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) * Experience in schema design, ontology management, and KG curation * Expertise in designing and developing KG solutions and graph-based ML models (functional + technical) * Experience with end-to-end data pipeline implementation for AI applications (esp. LLMs), with hands-on design and configuration * Experience with strong knowledge of relational databases, object stores, graph databases (Stardog, Neo4J, Amazon Neptune), and vector databases PREFERRED QUALIFICATION * Hands-on experience with cloud platforms (AWS, Azure, GCP) * Experience in Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow) * Practical experience with NLP and/or Search techniques * Prompt engineering, and LLMs for enterprise-scale applications. * You have team lead experience * Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones. * You have external client-facing consulting experience * Broad experience in diverse ML techniques and agentic systems ## Description * Build Knowledge Graph components that contribute to transforming a client's data architecture. * Design, develop, and implement AI and semantic solutions; ensure their work integrates cleanly with the broader system. * Work alongside the project team and delivery leads. * Build solid working relationships with client counterparts on their workstream. * Help assemble the supporting evidence for the recommended semantic layer solution. * Support Accenture sales efforts when called on. * Keep developing skills in cutting-edge Data & AI solutions, especially agentic technologies, and shares with the team. ## Related Videos - [Large Language Models ❤️ Knowledge Graphs](https://www.wearedevelopers.com/videos/1154-large-language-models-knowledge-graphs) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Knowledge graph based chatbot](https://www.wearedevelopers.com/videos/754-knowledge-graph-based-chatbot) - [Give Your LLMs a Left Brain](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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