Knowledge Engineer

Project Role : Knowledge Engineer Project Role Description : Design and structure knowledge frameworks that enable AI systems to reason and make informed decisions. Capture and translate expert and unstructured knowledge into ontologies, knowledge graphs, and semantic models, ensuring accuracy and context for automation and insights. Apply advanced analytics on knowledge graphs to drive problem-solving and actionable insights. Must have skills : Neo4j Good to have skills : Databricks Unified Data Analytics Platform Minimum 15 year(s) of experience is required Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Technical Architect role in Knowledge Engineering focused on leading enterprise-scale knowledge graph, semantic layer, ontology, and AI knowledge architecture solutions using Databricks lakehouse and AI capabilities. The role owns the complete knowledge engineering scope for strategic and complex programs, sets architecture standards, guides teams, shapes client solutions, and translates real-world business problems into scalable AI and knowledge graph solutions. The role must bring industry experience across domains such as BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, applying semantic AI, knowledge graphs, LLM grounding, governed data products, and Databricks-based data/AI architecture patterns to create reusable assets and measurable business value.

Key Responsibilities Define Databricks reference architectures for knowledge graph, semantic layer, and AI knowledge applications using Databricks Workspaces, Delta Lake, Unity Catalog, MLflow, Databricks SQL, Feature Engineering, Vector Search, Workflows, Model Serving, and cloud storage integrations. Architect lakehouse-centric data pipelines, graph ingestion, ontology management, feature and vector integrations, LLM grounding patterns, and governed knowledge access models on Databricks. Lead the complete Knowledge Graph and Knowledge Engineering solution scope that transforms data architecture for strategic, complex client programs. Own the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions across the program. Partner with project leaders, delivery leads, senior client stakeholders, architects, product teams, data engineers, AI engineers, and domain SMEs to create standout graph-powered offerings. Develop trusted-advisor relationships with senior client stakeholders and make a clear business case for semantic layer, knowledge graph, and enterprise AI knowledge architecture solutions. Lead architecture governance, design reviews, solution estimation, pre-sales support, proposal inputs, implementation planning, and technical risk management for complex knowledge engineering programs. Set standards for ontology design, semantic modeling, metadata management, data governance, lineage, knowledge graph curation, and reusable engineering patterns across programs. Build and mentor multidisciplinary teams, establish capability development plans, and guide delivery quality for knowledge engineers, data engineers, AI engineers, and platform specialists. Drive thought leadership, innovation, reusable assets, accelerators, and modern methods around knowledge graphs, semantic AI, LLM grounding, RAG, agentic systems, and graph-based AI patterns. Translate industry-specific business problems into scalable knowledge-driven architectures and reusable assets that advance the discipline beyond a single engagement.