Knowledge Engineer – Knowledge Graph, Agentic Interfaces

🕒 vor 3 Tagen

🇬🇧 Vereinigtes Königreich – Remote

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

👷🏻‍♀️ Ingenieur

🇬🇧 UK-Skilled-Worker-Visum-Sponsor

info

🗣️🇺🇸🇬🇧 Englisch erforderlich

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Logo of IFS

IFS

5001 - 10000 Mitarbeiter

Gegründet 1983

🏗️ Bauwesen

🏭 Fertigung

📦 Logistik

💰 Secondary Market im 2022-03

Construction • Manufacturing • Logistics

IFS ist ein Enterprise-Softwareunternehmen, das Lösungen für verschiedene Branchen anbietet, darunter Luft- und Raumfahrt sowie Verteidigung, Bau und Ingenieurwesen, Energie, Versorgungsunternehmen und Rohstoffe, Fertigung, Services und Telekommunikation. Das Unternehmen bietet ein breites Portfolio an Softwareprodukten, darunter Enterprise Asset Management, Field Service Management, Enterprise Resource Planning sowie KI-gestützte Lösungen wie IFS. ai. Diese Lösungen sind darauf ausgelegt, das Asset-Lifecycle-Management zu optimieren, die Kundenbindung zu verbessern und die operative Effizienz zu steigern. IFS Cloud stellt eine umfassende Plattform für die digitale Transformation bereit und unterstützt Unternehmen dabei, Daten zu nutzen und Erkenntnisse über ihre gesamten Geschäftsprozesse hinweg zu gewinnen.

Beschreibung

• Build and curate the ontology, knowledge graph and grounding infrastructure for AI agents and customer data • Design and build MCP servers over product business objects, including capability modelling, discoverability, versioning and backward compatibility • Build a write path enabling agents to safely change customer operational data • Design and build an ontology and knowledge graph with domain experts • Build retrieval and grounding infrastructure using embeddings, vector databases, hybrid search, chunking, indexing, memory architectures and grounding techniques • Establish data quality, provenance and versioning practices for the knowledge graph • Build a skills layer and router that maps user requests to correct operations and sequences • Build the control plane covering authentication, entitlements, agent identity, telemetry, metering, injection resistance and deny-by-default controls • Build an evaluation harness to certify agent behaviour against the real product • Develop rapid prototypes and proofs of concept for emerging technology, product opportunities and customer scenarios • Establish evaluation, testing, observability, monitoring, governance, security and operational excellence practices • Contribute to technical design and review other engineers’ work • Support colleagues entering the domain • Represent the work through customer engagements, demonstrations, industry events and partner collaboration

🎯 Anforderungen

• Demonstrable, hands-on experience designing, building and shipping production AI applications • Production experience building and operating enterprise systems • Deep experience in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD • Strong programming skills in a modern backend language • Experience delivering LLM systems, RAG, agentic workflows and orchestration frameworks • Experience with tool use, function calling, workflow orchestration and autonomous or multi-agent architectures • Hands-on expertise in knowledge graphs and semantic modelling • Experience with ontology design using RDF/OWL/SKOS or property-graph equivalents • Experience with 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 • Ability to work directly with domain experts to create explicit, machine-usable models • Depth in tool-surface and agent-runtime engineering or enterprise platform engineering • For enterprise platform depth: Oracle PL/SQL, OData and experience with large metadata-driven systems • Desirable: Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, OpenAI Agents SDK or CrewAI • Desirable: Docker and Kubernetes • Desirable: Azure, AWS, GCP or another hyperscale cloud platform • Desirable: 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

🏖️ Vorteile

• Remote and in-office working flexibility • Inclusive and diverse working environment • Commitment to sustainability • Opportunities for customer engagements, demonstrations, industry events and partner collaboration • Opportunity to work in a global, diverse environment

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