Job
- Level
- Experienced
- Job Field
- Software, Data
- Employment Type
- Part Time/Full Time
- Contract Type
- Permanent employment
- Location
- Essen
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will design semantic models, build and maintain knowledge graphs and retrieval pipelines to deliver context-based and explainable AI solutions for agents.
Job Technologies
Your role in the team
- As a hands-on Knowledge & Semantics Engineer, you build and operate the layer that provides our AI and agents with context.
- You will engineer the knowledge graphs, semantics and retrieval that agents reason over, turning domain know-how into reliable, governed context.
- Build and operate knowledge graphs and the semantic layer across domains, federated but interoperable, from modelling through to running pipelines.
- Implement master data management and business-rule logic, and maintain the ontology and shared semantics that connect domains.
- Engineer and optimise RAG and retrieval solutions, including chunking, embeddings, indexing, grounding, and evaluation, to deliver accurate, explainable, and reliable AI outcomes.
- Expose knowledge to agents as reliable, governed context and callable tools, including via MCP.
- Automate knowledge-curation pipelines and quality checks, and catalogue knowledge assets to avoid fragmented, unmanaged stores.
- Work with business experts to capture domain know-how and turn it into reusable knowledge products.
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Our expectations of you
Education
- University degree (or equivalent) in Computer Science, Computer Engineering, Information Technology or related field.
Qualifications
- Skills in building knowledge graphs and semantic models, with graph / semantic tooling (e.g. Stardog, Neo4j or similar) and query languages (SPARQL, Cypher or GraphQL).
- Skills in RAG engineering for LLMs and agents: embeddings, vector stores, retrieval pipelines, grounding and evaluation.
- Know-how of Master Data Management and ontology / taxonomy modeling (e.g., SAP MDG or equivalent).
- Strong data engineering foundations: Python, SQL, pipelines and CI/CD.
- The ability to translate business meaning into working technical implementation, and to elicit know-how from domain experts.
- Vertrautheit mit MCP und modernen Agenten-Frameworks.
Experience
- Hands-on working experience in handling data modelling and understanding of Data ecosystems.
- Experience grounding agents or LLM applications in enterprise knowledge at scale.
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What we offer
- Independent work on exciting and responsible tasks.
- Close collaboration in a motivated team with an open feedback culture that promotes your personal and professional development.
- Insights into a global energy company.
- Hybrid working and flexible working time models.
- Free access to the Health Center, modern open workspaces, free parking, e-charging stations and much more.
- Structured and digitized pre- and onboarding process supported by the web-based app RWE+You.
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Benefits
Work-Life-Integration
- 🏠Home Office
- 🅿️Employee Parking Space
- 🚌Excellent Traffic Connections
- ⏰Flexible Working Hours
- 🍼Day Care for Kids
More net
Health, Fitness & Fun
Food & Drink
Topics that you deal with on the job
Job Locations
This is your employer
RWE AG
RWE AG is an essential part of the European energy system, ensuring security of supply for Europe. With its three operating segments - lignite & nuclear power, European electricity generation from gas, coal, hydropower and biomass, and energy trading - it is one of the leading European energy companies.
Description
- Company Size
- 250+ Employees
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Power Sector, Economy
Knowledge & Semantics Engineer
- Location
- Essen
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
- English Only
- English only required
