Knowledge Engineer – Knowledge Graph, Agentic Interfaces

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Develop and curate the ontology, knowledge graph, and grounding infrastructure for AI agents and customer data.

• Design and construct MCP servers for product business objects, focusing on capability modeling, discoverability, versioning, and backward compatibility.

• Create a write path that enables agents to securely modify customer operational data.

• Collaborate with domain experts to design and develop an ontology and knowledge graph.

• Establish retrieval and grounding infrastructure utilizing embeddings, vector databases, hybrid search, chunking, indexing, memory architectures, and grounding techniques.

• Implement data quality, provenance, and versioning practices for the knowledge graph.

• Develop a skills layer and router that aligns user requests with the appropriate operations and sequences.

• Construct the control plane that encompasses authentication, entitlements, agent identity, telemetry, metering, injection resistance, and deny-by-default controls.

• Create an evaluation harness to validate agent behavior against the actual product.

• Rapidly prototype and develop proofs of concept for emerging technologies, product opportunities, and customer scenarios.

• Establish practices for evaluation, testing, observability, monitoring, governance, security, and operational excellence.

• Contribute to technical designs and review the work of other engineers.

• Assist colleagues in entering the domain.

• Represent the work through customer engagements, demonstrations, industry events, and partner collaborations.

⛳️ Requirements

• Proven, hands-on experience in designing, building, and deploying production AI applications.

• Experience in building and managing enterprise systems in a production environment.

• Extensive knowledge in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability, and CI/CD.

• Strong programming capabilities in a modern backend language.

• Experience in delivering LLM systems, RAG, agentic workflows, and orchestration frameworks.

• Familiarity with tool utilization, function calling, workflow orchestration, and autonomous or multi-agent architectures.

• Practical expertise in knowledge graphs and semantic modeling.

• Experience in ontology design using RDF/OWL/SKOS or equivalent property-graph models.

• Knowledge in taxonomy and controlled vocabulary design, entity resolution, schema evolution, and versioning.

• Experience with embeddings, vector databases, and grounding strategies.

• Evaluation experience, including experimentation, benchmarking, prompt engineering, tracing, quality measurement, and agent tuning.

• Ability to integrate enterprise applications, business processes, workflows, and data platforms.

• Capacity to work directly with domain experts to create explicit, machine-readable models.

• Depth in tool-surface and agent-runtime engineering or enterprise platform engineering.

• For enterprise platform expertise: Oracle PL/SQL, OData, and experience with large metadata-driven systems.

• Desirable: Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, OpenAI Agents SDK, or CrewAI.

• Desirable: Familiarity with Docker and Kubernetes.

• Desirable: Experience with Azure, AWS, GCP, or another hyperscale cloud platform.

• Desirable: Background in reverse-engineering or interpreter work, token-efficient agent design, relevant enterprise software domain experience, and contributions to open source, technical communities, conferences, publications, or standards.

🏝️ Benefits

• Flexibility for remote and in-office work.

• A welcoming and diverse working environment.

• Commitment to sustainability initiatives.

• Opportunities for customer engagements, demonstrations, industry events, and partner collaborations.

• Chance to work in a global and diverse setting.