20 beste Stellen als chat agent in berlin – aktuell auf Personalsuche

Senior AI / Knowledge Graph Engineer (m/f/d)
Pinnipedia Technologies GmbH —Berlin
- 60.000 € – 85.000 € pro Jahr
- Vollzeit
- Hybrides Arbeiten
- Master
- Künstliche Intelligenz
- Python
Direkt bewerben
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Xsolla —Berlin
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Synthflow AI —Berlin
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(Remote) Senior AI / Knowledge Graph Engineer (m/f/d)
Pinnipedia Technologies GmbH —Berlin
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- Master
- Künstliche Intelligenz
- Python
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German-speaking Customer Support Agent for Telecom Team in Athens
Next Job Abroad —Berlin
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Superchat —Berlin
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Superchat —Berlin
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Kaiko Systems GmbH —Berlin
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Synthflow AI —Berlin
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Staff/Principal Software Engineer (Frontend)
Parloa —Berlin
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Product Manager | Integrations (m/f/d)
Superchat —Berlin
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Principal Security Engineer
Pipedrive —Berlin
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Senior Software Engineer – AI-Driven Platform & Agentic Systems (m/w/d)
virtualQ GmbH —Berlin
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Director of Revenue (m/w/d)
Superchat —Berlin
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MySecondEar Audiology Group GmbH —Berlin
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Director of Engineering (m/f/d)
Superchat —Berlin
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Engineering Manager (m/f/d)
Superchat —Berlin
- Management-Erfahrung
- Java
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Senior AI / Knowledge Graph Engineer (m/f/d)

Pinnipedia Technologies GmbH
Berlin

Direkt bewerben

Details zur Stelle

Vollzeit
60.000 € – 85.000 € pro Jahr

Qualifikationen

Master
PostgreSQL
Neo4j
Künstliche Intelligenz
Graphdatenbanken
Python
Deutsch

Vollständige Stellenbeschreibung

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.

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: 60.000–85.000 € 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.

Direkt bewerben

Erweiterte Suche

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Senior Software Engineer – AI-Driven Platform & Agentic Systems (m/w/d)

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Ich möchte aktuelle Job-E-Mails für Stellen als chat agent in berlin erhalten

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