Ai Engineer (Knowledge Graphs & Llms)

Our client, aFortune 50 leaderin enterprise solutions and innovations, is seeking a Senior AI Engineer with Knowledge Graphs and LLMs skills to join their AI incubator to scout, incubate, and validate internal ideas. This role is part of a high-impact strategy leveragingGraph Neural Networks (GNNs) and Generative AIto redefine workflows, semantic search, and intelligence for enterprise solutions in Finance, Operations, Supply Chain, Engineering, or Investments. This is aremote-first position with candidates located in Europe,requiring overlap withUS working hours (2-6 PM CET). Responsibilities Build Agentic Workflows:Implement orchestration, retrieval pipelines, and validator agents using graph-aware tools. Optimize Retrieval:Build hybrid search pipelines (lexical + vector) and integrate vector databases like FAISS, Milvus, or Pinecone. Model Integration:Integrate LLMs (Azure OpenAI, Anthropic) and support domain-specific fine-tuning or adapter models. Scalable Engineering:Develop robust API endpoints and ETL pipelines to support model and agent runtimes. Experiment & Evaluate:Create evaluation suites for reliability, drift detection, and performance optimization., If you are passionate aboutAI, Graph-centric AI,Python, and building next-generationagentic workflows, this role with our client offers an exciting opportunity to work on cutting-edge R&D projects!

Python Expertise:3+ years of strong Python engineering experience. Graph Intelligence and Databases:Working knowledge of knowledge graph modeling (schemas, ontologies, entity resolution) and graph databases. Hands-on experience with Neo4j, Memgraph, AWS Neptune, ArangoDB, or similar. Familiarity with graph embeddings and GNNs (GCN/GAT) is a plus. Evaluation & Experimentation:Comfortable designing experiments, building eval harnesses, and reasoning about model quality, robustness, and bias in production AI systems. Modern AI Patterns:Hands-on experience building RAG pipelines and agentic workflows. Comfort with prompt engineering and tool/function calling. Experience building text-to-SQL or semantic parsing capabilities over structured data sources. LLM Observability:Familiarity with LLM evaluation frameworks (e.g., Ragas, DeepEval, Langfuse) and production monitoring of AI systems. Retrieval & Search:Lexical + vector + hybrid retrieval, embeddings, and reranking. Experience incorporating user and context signals for personalization. Fine-tuning & Adaptation:Experience with fine-tuning and adaptation patterns (e.g., LoRA/QLoRA, instruction tuning, embedding model fine-tuning). APIs & Integrations:Solid knowledge of APIs, microservices, and data-centric integrations. Engineering Discipline:Solid software engineering fundamentals - clean code, testing, debugging, code reviews, and comfort working in agile pods. Cloud & Deployment:Experience with AWS/Azure/GCP and CI/CD workflows. Excellent problem-solving skillsand keen attention to detail. Ability toparticipate in the discussionsand lead the technical discussions Have a consultancy mindset? always try to find a solution for the client