Senior AI / Knowledge Graph Engineer (m/f/d)

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.

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.

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.

• 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.

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

Competitive salary: 60\.000–85\.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 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.

Diese Anzeige stammt von indeed. Originalanzeige ansehen ↗