Knowledge Engineer

Intro: The Knowledge Engineer will lead the knowledge engineering scope within large programs to design, build and maintain ontologies, knowledge graphs and semantic models that enable AI systems to reason, support automation and deliver actionable insights. The role is client-facing, requires leading teams, contributing to sales/pre-sales, and driving technical direction, standards and thought leadership across business and technical stakeholders. Key responsibilities: - Lead design, development and implementation of knowledge graph (KG) and semantic solutions and ensure end-to-end integration with data and AI architectures. - Design and maintain ontologies, semantic models and knowledge representation standards; curate KG content and define schema/validation rules. - Build graph-powered data & AI offerings, justify semantic layer value to stakeholders, and contribute to pre-sales and solutioning. - Lead, mentor and make team-level decisions; collaborate with cross-functional engineering, research and product teams; support knowledge sharing and continuous learning. - Apply advanced analytics on knowledge graphs and design graph-based ML models and pipelines to derive insights and solve business problems. Required qualifications and experience: - Minimum 12 years of professional experience (posting specifies minimum 12 years), with stated requirement of 12 years experience in Snowflake Data Warehouse. - Bachelor’s degree or equivalent (15 years full-time education required). - Minimum experience across several areas (candidate should meet at least four of the listed): ~3+ years with knowledge graph technologies (RDF, SPARQL, LPG, SHACL); ~3+ years in schema design, ontology management and KG curation; ~3+ years designing/developing KG solutions and graph-based ML models; ~2+ years implementing end-to-end data pipelines for AI applications (esp. LLMs); ~4+ years with relational, object store, graph and vector databases; ~2+ years leading a team or workstream. - 2+ years hands-on with cloud platforms (AWS, Azure, GCP) and 2+ years with Python and ML frameworks (TensorFlow, PyTorch). Experience building ETL pipelines (Apache NiFi, Airflow). - Practical experience with NLP and/or search techniques; experience with prompt engineering and LLMs for enterprise-scale applications. Preferred / desirable: - Ph.D. in Computer Science, Computer/Electrical Engineering, Mathematics or related field. - Broad experience in diverse ML techniques and agentic systems; external client-facing consulting experience; strong collaboration across time zones. Tools, technologies and domain notes: - Emphasis on Snowflake as a core skill and Azure Data Services listed as good-to-have; explicit technologies called out include Stardog, Neo4J, Amazon Neptune, vector databases, RDF/SPARQL/LPG/SHACL, Python, TensorFlow, PyTorch, Apache NiFi and Airflow. - Role is based at Accenture’s Chennai office and is full-time; responsibilities include pre-sales contribution and publishing/presenting perspective beyond immediate project. Commitments and constraints: - This senior role expects team leadership, client engagement and the ability to set technical direction and standards. Education and minimum experience constraints are explicitly stated in the posting.