26 set - Milano
Generali Group
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
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