Research And Innovation Engineer
Pubblicato il 27-09-2026 - Generali Group in Livorno
Overview In this role you will advance language understanding and knowledge-grounded AI from research to production. You'll work closely with the AI Architect and Head of Technology to define use cases and set technical direction, balancing explainability, auditing, and trust for regulated contexts. You will build production-grade NLP services, design evaluation frameworks, and address failure modes and distribution shifts.
This role offers a hands-on opportunity to tackle hard, industry-driven problems with real impact.
Retribuzione / Benefits Lavoro da remoto and flexible hours Corporate welfare system Meal vouchers Supplementary health insurance Wellbeing initiatives Training and development Responsabilità Own the research-to-production arc for language understanding and knowledge-grounded AI from literature review to deployment and monitoring Design systems whose outputs are explainable and auditable, suitable for regulated contexts Build production-quality services around models: APIs, inference pipelines, evaluation harnesses Develop evaluation frameworks that test for failure modes, edge cases, and distribution shift Work on information extraction, semantic understanding,
and structured knowledge grounded in real industrial data Stay current with research in language models, interpretability, and trustworthy AI Contribute to technical standards and code quality across the team Tackle a genuinely hard, open research problem with direct industrial application Requisiti fondamentali ~ Master's degree or PhD in Computer Science, AI, Data Science, Engineering or related STEM field ~3–5 years of experience in NLP or language understanding systems in production environments ~3–5 years of experience in applied NLP or computer vision with real-world data and constraints ~ Experience across Healthcare, Financial Services, Mobility/Transport, or Agriculture sectors ~ Entrepreneurial mindset and ownership ~ Genuine interest in trustworthy and explainable AI as a design constraint ~ Excellent communication and stakeholder management, including interactions with C-suite and institutional clients ~ LLM-based systems, classical ML, APIs, microservices, inference optimization ~ Strong software engineering fundamentals (modular, testable, maintainable code) ~ End-to-end production lifecycle: model registry, latency considerations, monitoring