Accenture UK & Ireland
Knowledge Engineering Senior Analyst
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YOU ARE
You are 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.
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THE WORK
- 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 share with the team.
Basic (required) Qualification
- 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
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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.
- Team lead experience
- Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones.
- External client-facing consulting experience
- Broad experience in diverse ML techniques and agentic systems
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