Senior AI Engineer (Knowledge Graph)

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). 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.

• 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.

Existing team boundaries

A senior full-stack engineer already owns FastAPI architecture, infrastructure, auth, the public API surface, and the query abstractions / repositories exposed to product code.

This role owns everything from “document arrives” to “validated facts in the graph,” including the Cypher / graph access patterns and indexes those repositories sit on top of. It does not own:

• HTTP APIs, request/response schemas, or API versioning

• Application-level repository or query-builder abstractions

• Auth, infrastructure, deployment, or the FastAPI surface

Where the two roles meet (e.g. a new graph capability needs a new repository method), they collaborate -the AI/KG engineer specifies the graph contract and access pattern, the full-stack engineer owns how it’s exposed.

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.

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• 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.

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• German language skills.

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: 32.000–42.000 € 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 you’d use).

Process: 20-min intro → 90-min practical (graph modeling + retrieval evaluation) → 45-min team chat → references. We review applications within 5 business days.