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.
Your Mission -Own the end-to-end pipeline that turns unstructured documents into a validated queryable knowledge graph. Accountable for extraction quality graph integrity and the data layer that backs the products read path.
LLM extraction pipelines -document chunking property and relationship extraction cross-chunk reconciliation gap detection. Built with structured-output LLM agents orchestrated by durable workflows.
Knowledge graph -schema design as typed Pydantic models Cypher access patterns and indexing strategy graph operations schema evolution and migration. Scope ends at the graph boundary: API contracts and query abstractions exposed to consumers belong to the full-stack engineer.
Deterministic rule engines -table-driven evaluators for cases where code beats LLM judgment; clear contracts between deterministic and probabilistic components.
Data validation & quality -schema enforcement required-property contracts audit trails eval harnesses (expert review unsupervised checks synthetic fixtures LLM-as-judge).
Live data ops -backfills coordinated migrations across relational graph stores observability on extraction throughput and quality incident response.
Must-have
Nice-to-have
Remote full-time with flexible scheduling. CET (Berlin) timezone availability expected.
Possibility of relocation if successfull work relationship is achieved after a period of time.
Competitive salary: 32.00042.000 base (premium 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.
(Remote) Senior AI Knowledge Graph Engineer (mfd) • Berlin, Berlin, Germany
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