Ontology Engineer

Job Description (JD): Ontology Engineer

• Overall Experience: 5+ years
• AI Experience: 2.5+ years
• Primary Graph Platforms: Neo4j & GraphWise

• Approximately 5 years of overall experience across software engineering, data engineering, AI engineering or machine learning.
• At least 2.5 years of hands-on experience designing and developing agentic AI and Generative AI solutions.
• Demonstrated experience building enterprise-grade Knowledge Graph and GraphRAG solutions.
• Experience delivering production AI applications in cloud environments and integrating them with enterprise systems.
• Practical exposure to secure engineering, observability, evaluation, governance, and responsible AI controls.

• Design and implement enterprise Knowledge Graph architectures using Neo4j and Graphwise.
• Develop ontologies, taxonomies, entity models, semantic relationships, and metadata frameworks.
• Build graph ingestion, enrichment, and synchronization pipelines for structured and unstructured enterprise data.
• Implement of entity resolution, entity linking, graph traversal, inferencing, relationship discovery, and contextual intelligence.
• Use Knowledge Graphs as context, memory and reasoning layers for agentic AI solutions.
• Optimize graph schemas, indexes, queries, and analytics for performance and scale.

• Design and deploy agents capable of planning, reasoning, tool use, workflow execution, and controlled autonomy.
• Build single-agent and multi-agent architectures supporting orchestration, delegation, collaboration and task decomposition.
• Integrate Knowledge Graph context into agent planning, retrieval, and decision-making.
• Implement memory, state management, checkpointing, reflection, human-in-the-loop controls and long-running workflow patterns.
• Establish agent evaluation, traceability, governance, telemetry, and operational monitoring.

• Advanced Python development; strong working knowledge of TypeScript/JavaScript.
• Proficient in C#, Java and SQL
• API-first, microservices and event-driven architecture patterns.
• Azure experience across Azure OpenAI, Azure AI Foundry, Azure AI Search, Functions, Container Apps or AKS, API Management, Event Grid and Service Bus.
• Knowledge of identity, secrets management, network security, responsible AI and enterprise governance controls.

• Production experience designing and implementing enterprise Knowledge Graphs in Neo4j.
• Advanced Cypher query development, profiling and performance optimization.
• Graph schema and domain model design, including constraints, indexes, and relationship patterns.
• Hands-on use of Neo4j Graph Data Science for graph algorithms, embeddings or relationship analysis.
• Experience with Neo4j AuraDB and/or enterprise deployment patterns.
• Practical implementation of graph-enhanced retrieval and GraphRAG for LLM and agent solutions.

• Knowledge Graph construction, semantic enrichment and lifecycle management using Graphwise capabilities.
• Ontology-driven modelling, taxonomy management, and enterprise metadata integration.
• Entity extraction, entity linking and relationship enrichment across heterogeneous sources.
• Semantic search, reasoning and governed knowledge access for AI use cases.
• Integration of GraphWise-managed knowledge with RAG and agentic solution architectures.

• Hands-on experience with several major frameworks, with depth in at least two: Microsoft Semantic Kernel, LangGraph/LangChain, Azure AI Agent Service, Microsoft Copilot Studio or OpenAI Agents SDK.
• Implementation of tool/function calling, structured outputs, routing, planning and multi-agent collaboration.
• Experience with stateful and long-running workflows, checkpointing, durable execution and human approvals.
• Agent evaluation, prompt/version management, safety controls, tracing and production troubleshooting.