Knowledge Engineer

Intro: The Knowledge Engineer will lead the design, implementation and governance of knowledge frameworks (ontologies, semantic models and knowledge graphs) to enable AI systems to reason, automate and deliver actionable insights. The role includes technical leadership, client engagement, team management and innovation across a large program scope to translate real-world problems into scalable graph-powered AI solutions. Key responsibilities: - Lead the build, design and deployment of Knowledge Graph solutions and semantic layers that transform client data architectures. - Design, evaluate and maintain ontologies, semantic models and knowledge representation standards across programs. - Direct end-to-end integration of graph-based solutions with data pipelines, storage layers and AI applications (including LLMs). - Develop and apply advanced analytics on knowledge graphs to drive problem solving and business insights. - Guide model building techniques, graph-based ML and agentic systems exploration; provide thought leadership and contribute to pre-sales and solution positioning. - Manage, mentor and make decisions for a team/workstream; collaborate with cross-functional engineering, research and product teams and with client stakeholders. Required qualifications and experience (as stated): - Minimum 12 years total experience; 15 years full-time education required. - Bachelor’s degree or equivalent (Ph.D. listed as a qualification in the posting). - Minimum 3 years with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL). - Minimum 3 years in schema design, ontology management and KG curation. - Minimum 3 years designing and developing KG solutions and graph-based ML models (functional + technical). - Minimum 2 years in end-to-end data pipeline implementations for AI applications (especially LLMs). - Minimum 4 years’ experience with relational databases, object stores, graph databases and vector databases. - Minimum 2 years leading a team or workstream within a larger program. - 2+ years hands-on with cloud platforms (AWS, Azure, GCP). - 2+ years experience in Python and familiarity with frameworks like TensorFlow and PyTorch. - Practical experience with NLP and/or Search techniques; experience with prompt engineering and LLMs for enterprise-scale applications. Preferred / Good-to-have: - Microsoft Azure Data Services experience noted as good to have. - Broad experience in diverse ML techniques and agentic systems; demonstrated external client-facing consulting experience and strong collaboration skills across time zones. Tools, platforms and techniques referenced: - Snowflake Data Warehouse, Microsoft Azure Data Services, AWS, GCP. - Graph databases and tools: Stardog, Neo4j, Amazon Neptune, vector databases. - ML and deep learning: TensorFlow, PyTorch; Python for implementation. - ETL and pipeline orchestration: Apache NiFi, Apache Airflow. - Semantic technologies: RDF, SPARQL, SHACL; ontology design and KG curation. Location and other notes: - This position is based at Accenture’s Chennai office (on-site). - The posting emphasizes client-facing consulting, team leadership responsibilities, and contribution to sales/pre-sales. Compensation and benefits are not specified.