React · Python · Kubernetes · PyTorch · NLTK
As 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