Knowledge Engineer

Intro: Senior Knowledge Engineer responsible for designing and leading knowledge engineering scope within large programs to enable AI systems to reason and make informed decisions. The role focuses on capturing expert and unstructured knowledge and translating it into ontologies, knowledge graphs (KGs) and semantic models, applying analytics to drive insights and integrating KG solutions into data & AI architectures. Key responsibilities: - Lead design, development and implementation of knowledge graph and semantic solutions across program scope; ensure components integrate with broader data & AI systems. - Design, evaluate and maintain ontologies and knowledge models; establish standards and curate schema and KG assets. - Lead a team/workstream: mentor junior members, make team decisions, and coordinate across engineering, research, product and client stakeholder groups. - Work with project/delivery leads and clients to justify semantic layer solutions, contribute to pre-sales, and publish or present thought leadership. - Apply advanced analytics on knowledge graphs to derive actionable insights and solve business problems; contribute to graph-based ML and agentic systems. Required qualifications & experience: - Minimum ~12 years of relevant experience; team lead experience and client-facing consulting background. - Bachelor’s degree or equivalent; 15 years full-time education required per posting. - Must-have: proficiency in Microsoft Azure Data Services. - Practical experience: knowledge graph technologies (RDF, SPARQL, LPG, SHACL); schema design, ontology management and KG curation; designing and developing KG solutions and graph-based ML models. - Experience with end-to-end data pipeline implementations for AI (especially LLMs), relational databases, object stores, graph databases (Stardog, Neo4j, Amazon Neptune) and vector DBs. - 2+ years hands-on with cloud platforms (AWS, Azure, GCP); 2+ years Python with frameworks such as TensorFlow or PyTorch; experience with ETL tools (Apache NiFi, Airflow). - Practical experience with NLP and/or search techniques, prompt engineering and enterprise-scale LLMs. Preferred: - Ph.D. in Computer Science, Electrical/Computer Engineering, Mathematics or related field. - Broad experience in diverse ML techniques and agentic systems, plus demonstrable thought leadership. Location & other: - This position is based at Accenture’s Chennai office (on-site). Specific compensation not provided.