Knowledge Engineer

Intro: Senior Knowledge Engineer role responsible for designing and delivering knowledge frameworks (ontologies, knowledge graphs, semantic models) that enable AI systems to reason, support automation and produce actionable business insights. The role leads a team within a large program, sets technical direction, and drives innovation across business and technical stakeholders. Core responsibilities: - Lead the design, development and implementation of knowledge graph and semantic solutions across program scope; establish and maintain ontology and knowledge representation standards. - Capture and translate expert and unstructured knowledge into ontologies, knowledge graphs and semantic models; ensure accuracy, context and interoperability. - Direct graph-powered data & AI offerings, justify semantic layer value to business stakeholders, and contribute to sales and pre-sales activities. - Apply advanced analytics to knowledge graphs to drive problem solving and actionable insights; design and evaluate graph-based ML models. - Work across engineering, research and product teams; provide thought leadership, publish or present perspectives beyond the immediate project. - Lead, mentor and manage team decisions, foster knowledge sharing and provide guidance to junior members. Required qualifications & experience: - Minimum 12 years of professional experience (posting states “Minimum 12 year(s) of experience is required”). - Educational qualification: 15 years full-time education; Bachelor's degree or equivalent is stated. - Must-have skills: strong proficiency in Snowflake Data Warehouse and Microsoft Azure Data Services. - Practical experience with knowledge graph technologies (RDF, SPARQL, LPG, SHACL), schema design, ontology management and KG curation (minimum 3 years each listed). - Experience designing and developing KG solutions and graph-based ML models; experience with end-to-end data pipeline implementations for AI applications (esp. LLMs). - Strong knowledge of relational databases, object stores, graph databases and vector databases (several years’ experience and familiarity with Stardog, Neo4j, Amazon Neptune). - 2+ years hands-on with cloud platforms (AWS, Azure, GCP); 2+ years with Python and frameworks/tools such as TensorFlow, PyTorch and ETL pipeline tools (Apache NiFi, Airflow). - Practical experience with NLP and/or search techniques, prompt engineering and LLMs for enterprise-scale applications. - Team lead experience and client-facing consulting experience; strong cross-functional collaboration skills. Preferred / additional details: - The posting mentions Ph.D. in CS, CE, EE, Mathematics or related fields in the qualifications list (indicating research-level experience may be valued). - Broad experience across diverse ML techniques and agentic systems is indicated as desirable. Location & other material information: - This position is based at Accenture’s Chennai office (on-site). - The posting emphasizes thought leadership and contribution to large programs, including pre-sales and client engagement.