Ontology Engineer – Knowledge Graph & Agentic AI (Neo4j / Graphwise)

Job Summary

We are seeking an experienced Ontology Engineer with deep expertise in Knowledge Graphs, Agentic AI and enterprise AI architectures. You will design, build and deploy intelligent, context-aware solutions that combine semantic reasoning, RAG, GraphRAG and multi-agent orchestration to solve complex enterprise business challenges.

The role requires strong hands-on experience with Neo4j and Graphwise, using knowledge graphs as the foundational layer for ontology definition, agent context, memory, reasoning, retrieval and decision support. You should also be comfortable integrating agentic frameworks, LLMs, enterprise APIs and cloud-native services into secure, scalable production solutions.

Key Responsibilities

Ontology Engineering

• 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 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.

Agentic AI Development

• 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.

Required Qualifications

• About 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.

Required Technical Skills

Neo4j

• 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.

Graphwise

• Knowledge Graph construction, semantic enrichment and lifecycle management using Graphwise.
• 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.

Agentic AI Platforms

• 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.
• Tool/function calling, structured outputs, routing, planning and multi-agent collaboration.
• Stateful and long-running workflows, checkpointing, durable execution and human approvals.
• Agent evaluation, prompt/version management, safety controls, tracing and production troubleshooting.

Programming, Cloud & Integration

• Advanced Python; 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.

Tools & Technologies

• Knowledge Graph: Neo4j, Cypher, Neo4j Graph Data Science, Neo4j Bloom, Neo4j AuraDB, Graphwise, RDF, OWL, SPARQL
• Agentic AI: Microsoft Semantic Kernel, LangGraph, LangChain, Azure AI Agent Service, Microsoft Copilot Studio, OpenAI Agents SDK
• Retrieval & Search: Neo4j GraphRAG, Azure AI Search, LlamaIndex (RAG, vector and hybrid search, semantic ranking, reranking)
• Integration: Python, TypeScript/JavaScript, C#, SQL, FastAPI, Flask, .NET, REST, GraphQL, gRPC, Pydantic, pytest

Nice to Have

• DevOps/MLOps: CI/CD, containerization, infrastructure as code and release management with Azure DevOps, GitHub Actions, Docker, Kubernetes and Microsoft Entra ID.