17 ott - Modena
Expert.Ai
Questa posizione è in Expert.ai
Il processo di selezione sarà interamente gestito Expert.ai.
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**About expert.ai** 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. **The Role** We 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 Do** * Design and implement RAG pipelines end-to-end: ingestion, chunking strategies, embedding models, vector retrieval, reranking, and response generation * Build agentic workflows using LangChain, LangGraph, LlamaIndex, or custom orchestration — including tool use, memory management, and multi-step reasoning * Integrate and prompt-engineer LLMs (GP Claude Sonnet/Opus, Qwen)
for domain-specific tasks; contribute to fine-tuning efforts when needed * Develop MCP servers and clients to standardize tool and context exposure across AI systems * Maintain vector databases (FAISS, Pinecone, Weaviate, Qdrant, pgvector) and optimize retrieval quality * Build evaluation pipelines to track hallucination rate, retrieval precision, latency, and output consistency over time * Write clean, tested, production-ready Python and contribute to code reviews * Collaborate with senior engineers and clients to translate requirements into solid technical decisions **What We Expect** * 2 to 4 years of software engineering experience, with at least 1 to 2 years focused on LLM or applied AI systems * At least one production RAG or agentic application under your belt — you know what it took to get it there * Solid understanding of embeddings, transformer fundamentals, context management, and prompt design patterns * Familiarity with Docker * Able to work with autonomy — you ask good questions, but you do not wait to be told what to do next Formal degrees are welcome but not required. What matters is what you have shipped. **Nice to Have** * Experience with MCP protocols in real projects * Knowledge graphs or GraphRAG pipelines (Neo4j, Neptune, or similar) * Inference optimization: quantization (GGUF, AWQ, GPTQ), vLLM * Evaluation tooling: RAGAS, TruLens, or custom eval design * Domain NLP experience in legal, manifacturing, or healthcare
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