Knowledge Engineer

Intro: The Knowledge Engineer leads the design and delivery of knowledge frameworks (ontologies, semantic models, knowledge graphs) that enable AI systems to reason, support automation and generate actionable insights. This is a senior, client-facing role within a large program scope that includes team leadership, technical direction, thought leadership and pre-sales contributions. The role is based at Accenture’s Chennai office and requires extensive hands-on and leadership experience. Key responsibilities: - Design, structure and maintain ontologies, knowledge graphs and semantic models to represent complex knowledge domains and ensure contextual accuracy. - Lead build and implementation of Knowledge Graph solutions that transform client data architectures and integrate with AI/semantic platforms. - Direct design, development and deployment of AI and semantic solutions, ensuring components interoperate across data and application stacks. - Apply advanced analytics on knowledge graphs to derive problem-solving insights and business value. - Work with project/delivery leads and client stakeholders to justify semantic-layer solutions and contribute to sales/pre-sales. - Lead and mentor team members, make team-level technical decisions, and coordinate across cross-functional engineering, research and product teams. Required qualifications & experience: - Minimum 12 years of professional experience (posting lists minimum 12 years). - Educational requirement stated as 15 years full-time education; listing also references Bachelor’s degree or equivalent. - Must-have: proficiency in Microsoft Azure Data Services. - Minimum multi-year experience across KG technologies and design: e.g., 3+ years with RDF/SPARQL/LPG/SHACL; 3+ years in schema design, ontology management and KG curation; 3+ years designing KG solutions and graph-based ML models; 2+ years with end-to-end data pipelines for AI (esp. LLMs). - 4+ years familiarity with relational DBs, object stores, graph DBs (Stardog, Neo4J, Amazon Neptune) and vector databases. - 2+ years leading a team/workstream and 2+ years hands-on cloud experience (AWS, Azure, GCP). - 2+ years Python experience and familiarity with frameworks/tools such as TensorFlow, PyTorch, Apache NiFi, Airflow. - Practical experience with NLP and search techniques; experience with prompt engineering and enterprise-scale LLMs. Preferred / additional: - Ph.D. in Computer Science, Electrical Engineering, Mathematics or related field (listed in posting). - Broad experience in diverse ML techniques and agentic systems. - Strong client-facing consulting experience and ability to present/publish thought leadership. Tools & technologies explicitly referenced: Microsoft Azure Data Services, AWS, GCP, Stardog, Neo4J, Amazon Neptune, vector databases, RDF, SPARQL, SHACL, Python, TensorFlow, PyTorch, Apache NiFi, Airflow, LLMs, NLP, search techniques, prompt engineering. Location & work mode: On-site at Chennai office. No salary was provided in the posting.