AI Engineer

• Sviluppare soluzioni di Artificial Intelligence attraverso la scrittura di codice robusto, scalabile e manutenibile, adottando best practice di sviluppo e favorendo il riuso di componenti e librerie
• Progettare, implementare e ottimizzare modelli, pipeline e servizi AI, inclusi agenti intelligenti e sistemi conversazionali
• Gestire il ciclo di vita dei modelli di Machine Learning, dal training e dalla validazione fino al deployment e al monitoraggio in produzione
• Implementare pipeline MLOps e CI/CD automatizzate, garantendo qualità, affidabilità e tracciabilità dei rilasci
• Sviluppare e manutenere API, microservizi e connettori per integrare le soluzioni AI con i sistemi aziendali
• Gestire database relazionali e non relazionali, con focus sui vector database a supporto di applicazioni LLM e architetture RAG
• Progettare e sviluppare applicazioni basate su Large Language Models (LLM), AI Agent e soluzioni Retrieval-Augmented Generation (RAG)
• Supportare la scalabilità e l’industrializzazione delle soluzioni AI in produzione, implementando processi di monitoraggio, osservabilità, versionamento del codice (Git) e gestione di ambienti containerizzati con Docker e Kubernetes

• Partner with business stakeholders to identify, prioritize, and deliver AI use cases aligned to strategic objectives.
• Act as Technical Product Owner (TPO) for AI products and platforms, managing backlog prioritization and roadmap execution.
• Translate business requirements into actionable technical epics, features, and user stories.
• Drive adoption of AI solutions by ensuring measurable business outcomes and user satisfaction.
• Design, develop, and deploy Machine Learning, Predictive Analytics, Generative AI, and Agentic AI solutions.
• Lead model lifecycle activities including experimentation, training, validation, deployment, monitoring, and continuous improvement.
• Establish engineering best practices covering MLOps, model governance, performance monitoring, and operational support.
• Ensure AI solutions are scalable, secure, maintainable, and compliant with enterprise standards.
• Collaborate closely with Data Engineers to design trusted, reusable, and governed data assets.
• Support the creation of robust data pipelines required for AI development and operationalization.
• Drive integration of structured and unstructured data sources across enterprise platforms.
• Contribute to AI and Data Platform architecture decisions to improve scalability and reuse.
• Lead Agile squads delivering AI products and capabilities across multiple business domains.
• Facilitate sprint planning, backlog refinement, retrospectives, and delivery governance activities.
• Remove delivery obstacles and ensure predictable execution against committed objectives.
• Promote DevOps and Agile best practices across the squad.
• Act as the bridge between business teams, product managers, architects, and engineering teams.
• Ensure AI solutions comply with security, privacy, responsible AI, and regulatory requirements.
• Define and monitor OKRs/KPIs for AI solution performance, business value realization, adoption, and operational effectiveness.

Technical Product Owner – Lead AI Engineer

• Develop and maintain AI and Machine Learning solutions based on business and technical requirements.
• Participate in the design, testing, and deployment of AI-enabled applications.
• Support the implementation of Generative AI, LLM, and Retrieval-Augmented Generation (RAG) capabilities.
• Participate in model evaluation, performance monitoring, and solution optimization.
• Collaborate with Data Engineers and Software Engineers to access and prepare data required for AI solutions.
• Contribute to data integration and feature engineering activities.
• Support implementation of AI services within approved enterprise platforms.
• Assist in maintaining data quality and data governance requirements.
• Participate actively in Agile ceremonies including sprint planning, stand-ups, retrospectives, and backlog refinement.
• Deliver committed user stories and technical tasks within sprint commitments.
• Support testing, deployment, and release activities.
• Escalate risks and technical issues when appropriate.
• Follow established AI Engineering, MLOps, and DevOps standards and practices.
• Contribute to documentation, code reviews, and technical knowledge sharing.
• Support monitoring and maintenance activities for deployed AI solutions.
• Apply security, privacy, and responsible AI requirements throughout development activities.
• Work closely with Product Owners, Business Analysts, Architects, and senior engineers while supporting solution implementation activities.
• Support business requirement clarification and technical solution discussions.
• Communicate progress, blockers, and technical findings to project teams.
• Stay current with emerging AI technologies and engineering practices.
• Participate in technical communities and learning initiatives.
• Identify opportunities for process improvements and support implementation of approved enhancements.

Senior Analyst – AI Engineering

• Entrerai nel team specializzato in soluzioni di Artificial Intelligence della nostra Practice
• Lavorerai su progetti enterprise reali — in produzione, non in sandbox
• Collaborazione in team multidisciplinari a stretto contatto con clienti e stakeholder

AI Developer

• Progettazione e sviluppo di soluzioni avanzate di Machine Learning e Generative AI, con l’utilizzazione di architetture moderne in cloud (AWS, Azure, GCP) per la realizzazione di pipeline AI scalabili e performanti.

• AI Product Development: Progetterai e implementerai feature basate sia su Generative AI (fine-tuning e orchestrazione di LLM come Llama o OpenAI) che su modelli di Machine Learning classico.
• Bridge Tech-Business: Lavorerai a stretto contatto con il Product Team per identificare i casi d'uso ad alto impatto, traducendo i bisogni di business in progetti di data science concreti.
• MLOps & Infrastructure: Non scriverai solo modelli, ma contribuirai a costruire l'infrastruttura per il prompt management, il monitoraggio dei modelli, l'ottimizzazione dei costi e le pipeline di deploy.
• Continuous Improvement: Monitorerai le performance dei modelli in produzione, analizzando i dati di utilizzo per iterare e garantire che l'AI porti vero valore al business.

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