Senior AI Knowledge Graph Engineer (mfd)

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.

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.

Requirements

Must-have

• 5 years shipping data/AI systems to production with real customers -has been on-call for live pipelines and knows what breaks at 2am.
• 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.
• Production LLM pipeline experience: structured output agent orchestration prompt and version management evaluation frameworks. PydanticAI LangChain DSPy or Instructor all welcome.
• Durable workflow orchestration in production (DBOS Temporal Airflow Prefect Dagster).
• Test-first discipline -integration tests against real datastores (Testcontainers or equivalent) not mock-heavy unit tests.
• Fluent English skills.

Nice-to-have

• Experience with regulated compliance-driven or standards-heavy extraction domains (legal medical financial security/audit).
• Designed deterministic evaluators alongside LLM components and knows when to reach for which.
• Contributions to data contracts schema governance or ontology work.
• German language skills.

Benefits

Hybrid full-time with flexible scheduling; occasional on-site days in Berlin.

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.