Pinnipedia is a new Berlin startup building a cloud platform that automates and assists the creation of audit-ready IT-security concepts (e.
, BSI-Grundschutz, C5).
We’re IGP-funded (2025/26) and co-develop with FU Berlin and pilot users from industry and security consulting.
We’re 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 product's read path.
Tasks • 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.
Strong Python (typed, modern) and SQL.
Comfortable with PostgreSQL under load.
Production experience with at least one graph database (Neo4j preferred; Neptune, ArangoDB, TigerGraph acceptable) -schema design, query tuning, not toy use.