AI Engineer (LLMs + Knowledge Graphs) (mfd)
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Job Location:
Yearly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Pinnipedia is a new Berlin startup building a cloud platform that automates and assists the creation of audit-ready IT-security concepts (e.g. BSI-Grundschutz C5). Were IGP-funded (2025/26) and co-develop with FU Berlin and pilot users from industry and security consulting.
Were hiring an AI Engineer to turn messy inputs into structured knowledge and reliable answers. Youll design and operate knowledge-graph LLM (RAG) pipelines model/ingest domain ontologies and own evaluation so we can ship trustworthy features.
Tasks
Knowledge graphs & data (40%)
- Model the domain (ontology/taxonomy); build ETL into a graph store.
- Author queries (SPARQL/Cypher) and surface graph facts and relationships in features.
RAG & LLM integration (30%)
- Design retrieval and answer generation workflows (indexing chunking reranking).
- Orchestrate prompts/tools; balance KG vector search and business rules.
Evaluation & quality (20%)
- Define and track retrieval/answer metrics (e.g. precision/recall faithfulness).
- Build test fixtures and regression checks; monitor drift and data quality.
Production & collaboration (10%)
- Ship well-tested Python components (FastAPI jobs/services); document decisions; work from a clear backlog with PO and engineers.
Requirements
Must-have
- Strong Python and data engineering fundamentals.
- Hands-on with knowledge graphs (ontology design queries) and a graph DB.
- Practical RAG experience (indexing retrieval evaluation).
- Testing mindset (pytest) version control and clear documentation.
- English required (German nice-to-have).
Nice-to-have
- Security/compliance awareness; prompt/agent tooling; spaCy/Transformers.
- Observability for ML/LLM systems; simple dashboards for quality metrics.
- Cloud basics (AWS/Azure) containers (Docker); CI/CD.
Benefits
Hybrid full-time with flexible scheduling; occasional on-site days near Berlin/Brandenburg (Ketzin/Havel).
Competitive salary: 60.00085.000 base (more for exceptional senior profiles).
Small focused team; direct collaboration with the Product Owner and Full-Stack Engineer.
Modern tooling real ownership and a learning budget for role-relevant training.
Impact: help SMEs meet rising security requirements with less friction.
Apply on JOIN with your CV (PDF) and a short note (max 200 words) describing how you would design a KG-backed RAG pipeline (ontology scope indexing retrieval and evaluation youd use).
Process: 20-min intro 90-min practical (graph modeling retrieval evaluation) 45-min team chat references. We review applications within 5 business days.
Key Skills
- Graph Databases
- Data Analytics
- AI
- Ontology
- OWL
- RDF
- SPARQL
- Natural Language Processing
- Neo4j
- Knowledge Management
- Taxonomy
