Control Risks

Technology

SeniorAIEngineer

€85–125k ~AI est. Berlin, Berlin, Germany FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior AI Engineer at Control Risks. Skills: Knowledge graph, Agentic pipelines, LLM integration, Data engineering. Own knowledge graph data model. Evolve knowledge graph data model”

Industry & Context.

Technology
Problems you'll solve

Root cause analysis; Troubleshooting; Data-driven decision making

What They're Looking For.

Must Have

3+ years AI/ML engineering, 1+ year knowledge graphs, Hands-on Neo4j experience, Familiarity with LLM APIs, Understanding LLM trade-offs, Experience building agentic pipelines, Python proficiency, Experience with RAG architectures, Ability to read research papers, English communication skills

Nice to Have

Graphiti experience, Entity resolution experience, NER pipelines experience, Topic modelling experience, Information extraction experience, Third-party risk knowledge, Sanctions screening knowledge, KYC/AML domain knowledge, Databricks experience, Delta Lake experience, GCP exposure, Vertex AI experience, Cloud Run experience, Pub/Sub experience, Cloud Storage experience, Evaluation frameworks experience, RegTech experience, FinTech experience, Compliance-adjacent experience

What You'll Do.

Own knowledge graph data model

Evolve knowledge graph data model

Implement entity extraction pipelines

Extend entity extraction pipelines

Use Graphiti as memory layer

Manage episode ingestion

Manage entity resolution

Design graph schema standards

Enforce graph schema standards

Build evaluation frameworks

Design agentic workflows

Implement agentic workflows

Orchestrate LLM calls

Integrate AI web search

Implement graph interrogation layer

Manage LLM API integrations

Build data ingestion pipelines

Maintain data ingestion pipelines

Implement entity disambiguation

Develop topic modelling pipelines

Integrate with .NET backend

Stay current with literature

Evaluate new frameworks

Evaluate new techniques

Contribute to architectural decisions

Maintain Claude Code context

Write technical documentation

How You'll Work.

Team & Collaboration

Work with AI team; Work with CTO; AI-augmented development

Communication Scope

English communication; Technical documentation

Full Job Description

As Senior AI Engineer you will own the AI and data layer of the platform. You will design and implement the knowledge graph architecture, build agentic pipelines that enrich entity data through AI web research, and develop the graph interrogation layer that translates natural language compliance questions into structured graph queries. You will work closely with the CTO and a small AI team including a data engineer and a junior AI engineer. You will have significant technical autonomy and direct influence over how the product evolves. **Please submit yoru CV in English.** **Requirements** ### What You’ll Work On **Knowledge graph architecture and implementation** • Own and evolve the platform’s knowledge graph data model — entities (Company, Person, Community, Episodic), relationships, and temporal attributes • Implement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring • Use Graphiti (Zep AI’s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates • Design and enforce graph schema standards, ensuring consistency across data sources and ingestion pipelines • Build evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage **Agentic AI pipelines** • Design and implement multi-step agentic workflows that orchestrate LLM calls, web search, database lookups, and graph writes • Integrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment • Build retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers • Implement the graph interrogation layer — a conversational interface for compliance analysts to query the knowledge graph without writing Cypher • Manage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, st

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