Senior ai software engineer m/f/x
Pubblicato il 04-08-2026 - Nash Technologies in Italia
Nash Technologies is seeking a Senior AI Software Engineer (LLM & RAG Systems) m/f/x in Boeblingen to support the design, development and optimization of enterprise-grade LLM and RAG solutions. The candidate will be responsible for the end-to-end implementation and integration of AI systems into our web and documentation platforms, working closely with cross‐functional teams to deliver robust, production‐ready solutions. Your profile: Master's or Ph. D. in computer science, machine learning, software engineering or related field Minimum 2 years of hands‐on industry experience in AI engineering and software development Proven experience in LLM/RAG development and Python frameworks (e.g., Sentence Transformers, Pandas, Num Py) Experience with vector databases (e.g., Chroma), RAG evaluation frameworks, and open‐source LLMs (e.g., Qwen, Llama) Clear communication and presentation skills; strong team player Excellent English language skills Good To Have Full‐stack development experience, especially integrating AI models into web applications (e.g., Fast API, MCP) Skills in Java,
Eclipse IDE, and web technologies (Java Script, HTML5, CSS3, XML, XSLT) Your responsibilities: Design, implement, test and optimise end‐to‐end Retrieval‐Augmented Generation (RAG) pipelines, including data parsing, ingestion, prompt engineering and chunking strategies Evaluate and deploy Large Language Model (LLM) applications, focusing on metrics such as correctness, latency and hallucination Integrate AI components into web applications and documentation platforms using Java, Eclipse IDE and related technologies Write clean, maintainable and testable code following best practices in both Python and Java Build and release product documentation updates using Jenkins CI Provide technical support and development for a responsive web application (online help system) based on Eclipse What we offer: A permanent position in an exciting and growing team Hybrid work model Flat hierarchies and flexible wo