Ai & Knowledge Graph Engineer
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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 (Lang Chain, Semantic Kernel, etc.)Understanding of perceptual hashing (p Hash, a Hash, d Hash) 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, Py Torch, Hugging Face, Lang Chain, Qdrant/Weaviate/Milvus, Open AI/Gemini API, RDFLib, Graph DB, SPARQL, FAISS, Tensor Flow, Fast API, Docker, Git Hub Actions, MCPWe offer a market‑aligned salary, participation in employee stock option pool and of course growth into companies’ leadership positions.
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