Knowledge Engineering Manager

YOU ARE

You lead a significant knowledge engineering scope and function within a large program; actively contribute to thought leadership. You shape how a substantial body of work formulates real‑world problems into scalable AI and KG solutions, and set the technical direction for that scope. You lead a team and guide exploration and implementation of new methodologies, model building techniques, and cutting‑edge algorithms. You remain at the forefront and drive innovation by applying these techniques to new business problems, use cases, and scenarios. You design, evaluate, and maintain ontologies, and establish the standards others follow. You justify the value to business and technology stakeholders, and construct the methodologies and data architectures that demonstrate it. You publish or present perspective beyond the immediate project. You work across business and technical teams to drive end‑to‑end delivery for their scope.

THE WORK

• Leads the build of Knowledge Graph solutions that transform a client's data architecture within program scope.
• Directs the design, development, and implementation of AI and semantic solutions, ensuring all the pieces work together seamlessly.
• Works with project leaders, delivery leads, and client stakeholders to create stand‑out, graph‑powered Data & AI offerings.
• Develops strong client relationships and earns the trust of key advisors.
• Makes the business case for the semantic layer solution recommended to the client.
• Contributes meaningfully to Accenture sales and pre‑sales efforts.
• Provides thought leadership on technology trends, new opportunities, innovations, and foreseeable limitations, risks, and concerns.

EDUCATION

• Bachelor's degree or equivalent

Basic (required) Qualification

• Bachelor's degree or equivalent, plus at least 4 of the following:
• Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL)
• Solid experience in schema design, ontology management, and KG curation
• Proven experience in designing and developing KG solutions and graph‑based ML models (functional + technical)
• Experience with end‑to‑end data pipeline implementations for AI applications (esp. LLMs), with hands‑on design and configuration
• Strong knowledge/working experience of relational databases, object stores, graph databases (Stardog, Neo4J, Amazon Neptune), and vector databases