About We build production-grade AI systems for enterprise clients. Our work focuses on Large Language Models, Retrieval-Augmented Generation, agentic architectures, and knowledge-driven AI. We are a lean team of engineers and researchers who move fast and care about the quality of what we ship.Se i requisiti e l'esperienza richiesti per questo lavoro corrispondono alle sue competenze, la preghiamo di candidarsi tempestivamente.The RoleWe are looking for an AI Engineer with around 2 to 4 years of experience — someone past the learning phase, who has shipped at least one LLM-powered system to production and knows what breaks, what scales, and what was a bad idea in hindsight.You do not need to have done everything. You need to have done some things well, understand why they worked, and be ready to go deeper.What You Will DoDesign and implement RAG pipelines end-to-end: ingestion, chunking strategies, embedding models, vector retrieval, reranking, and response generationBuild agentic workflows using LangChain, LangGraph, LlamaIndex, or custom orchestration — including tool use, memory management, and multi-step reasoningIntegrate and prompt-engineer LLMs (GP Claude Sonnet/Opus, Qwen)



for domain-specific tasks; contribute to fine-tuning efforts when neededDevelop MCP servers and clients to standardize tool and context exposure across AI systemsMaintain vector databases (FAISS, Pinecone, Weaviate, Qdrant, pgvector) and optimize retrieval qualityBuild evaluation pipelines to track hallucination rate, retrieval precision, latency, and output consistency over timeWrite clean, tested, production-ready Python and contribute to code reviewsCollaborate with senior engineers and clients to translate requirements into solid technical decisionsWhat We Expect2 to 4 years of software engineering experience, with at least 1 to 2 years focused on LLM or applied AI systemsAt least one production RAG or agentic application under your belt — you know what it took to get it thereSolid understanding of embeddings, transformer fundamentals, context management, and prompt design patternsFamiliarity with DockerAble to work with autonomy — you ask good questions, but you do not wait to be told what to do nextFormal degrees are welcome but not required. xysqume What matters is what you have shipped.Nice to HaveExperience with MCP protocols in real projectsKnowledge graphs or GraphRAG pipelines (Neo4j, Neptune, or similar)Inference optimization: quantization (GGUF, AWQ, GPTQ), vLLMEvaluation tooling: RAGAS, TruLens, or custom eval designDomain NLP experience in legal, manifacturing, or healthcare#J-18808-Ljbffr

📌 Ai Engineer (Modena)
🏢 Expert System
📍 Modena