AI & Knowledge Graph Engineer

Umanitek Ag; Zürich; On-site; CHF 90,000 - 120,000; Full time

Benefits offered by this job

Participation in employee stock option pool

Growth into leadership positions

Job summary

Umanitek Ag in Zürich is seeking an AI & Knowledge Graph Engineer to develop and optimize systems that detect deepfakes and identity abuse. The role involves integrating various multimedia analysis tools and ensuring the scalability and reliability of the Umanitek Guardian pipeline. Candidates should have a strong background in data engineering and knowledge graphs. The position offers a competitive salary and opportunities for growth within the company.

Qualifications

• Strong background in production ETL and pipeline systems.
• Experience with knowledge graphs, especially RDF, SPARQL, and reasoning approaches.
• Understanding of perceptual hashing methods.

Responsibilities

• Develop and optimize hybrid reasoning systems.
• Integrate media-analysis capabilities for content discovery.
• Measure and improve detection performance.

Skills

Data engineering

Knowledge graphs

SPARQL

LLMs

Image/video processing

Tools

Python

Rust

Nodejs

PyTorch

Docker

Job description

The Umanitek Guardian Agent protects people from deepfakes, identity abuse, and coordinated harmful narratives by combining Decentralized Knowledge Graphs (DKG) with graph-native risk intelligence. If you’re excited by building resilient data infrastructure and operational systems for agents that reason over verifiable evidence, not just predict from black-box signals, you’ll feel right at home.

As a AI & Knowledge Graph Engineer, you’ll work at the frontier of neuro-symbolic AI, connecting LLM-assisted analysis with structured graph reasoning and provenance. You’ll be responsible for building and evolving our core product – the Umanitek Guardian pipeline and agent network – an AI system that protects humans from deepfakes and impersonation by grounding intelligence in RDF knowledge graphs. Your focus will be to design and apply hybrid reasoning capabilities, combining SPARQL-driven graph logic, risk scoring, entity resolution, embedding similarity, and media analysis into one coherent threat-detection system.

Responsibilities:

• Develop and optimize hybrid reasoning systems combining Knowledge Graphs, LLMs, embedding search, and symbolic reasoning
• Integrate media-analysis capabilities (image/video processing, OCR, similarity signals) for harmful content discovery and tracking
• Integrate perceptual hashing and similarity search algorithms for image/video discovery
• Build and evolve the Guardian stack (RDF, SPARQL, provenance modeling, schema/ontology design) for neuro-symbolic reasoning
• Build and test MCP tools enabling Guardian’s AI to safely query other Guardian Agents on the Guardian Network
• Measure and improve risk scoring, graph reasoning quality, and end-to-end detection performance
• Ensure scalability, data quality, and operational reliability of the Guardian knowledge base and orchestration pipeline

Technical experience that is most relevant to us:

• Strong background in data engineering and production ETL/pipeline systems
• Experience with knowledge graphs, particularly RDF, SPARQL, Blazegraph, and reasoning approaches
• Experience with LLMs, embeddings, or reasoning systems (LangChain, Semantic Kernel, etc.)
• Understanding of perceptual hashing (pHash, aHash, dHash) and multimedia similarity search
• Understanding of multimedia threat analysis, including similarity methods and practical approaches to image/video signal matching

Tech Stack and Tools: Python, Rust, Nodejs, PyTorch, HuggingFace, LangChain, Qdrant/Weaviate/Milvus, OpenAI/Gemini API, RDFLib, GraphDB, SPARQL, FAISS, TensorFlow, FastAPI, Docker, GitHub Actions, MCP

We offer a market‑aligned salary, participation in employee stock option pool and of course growth into companies’ leadership positions.