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Campari Group today is a major player in the global branded spirits industry, with a portfolio of over 50 premium and super premium brands, marketed and distributed in over 190 markets around the world, with leading positions in Europe and the Americas. Headquartered in Milan, Italy, Campari Group owns 25 plants worldwide and has its own distribution network in 26 countries, and employs approximately 4,700 people. Shares of the parent company Davide Campari - Milano N.V. are listed on the Italian Stock Exchange since 2001. Campari Group is today the sixth-largest player worldwide in the premium spirits industry. Mission The Global Machine Learning Engineer is responsible for accelerating the delivery, industrialisation and scaling of Machine Learning capabilities across Campari Group, with a particular focus on Revenue Growth Management, forecasting, pricing optimisation, promotional effectiveness and commercial analytics. The role transforms advanced analytical prototypes into robust, reusable and production-ready AI products that improve decision quality, increase automation, reduce external dependency and unlock measurable revenue, margin and operational efficiency benefits. General Description of the Role Within the Technology & Services organization, the AI, Data & Analytics team, the Global Machine Learning Engineer is responsible for enabling data-driven decision making and accelerating business value through data, analytics and Artificial Intelligence capabilities. The Global Machine Learning Engineer plays a critical role in designing, developing, deploying and maintaining machine learning models, optimisation engines and production-grade AI solutions that support strategic initiatives across Revenue Growth Management, demand forecasting, pricing optimisation, promotion effectiveness, sales planning and commercial decision-making. The role bridges data science, data engineering, business stakeholders and technology platforms, ensuring that AI and ML models move from proof-of-concept into robust, secure, monitored and reusable products that can be deployed and adopted at global scale. The Machine Learning Engineer will contribute to MLOps practices, automated model retraining, model monitoring, explainability and governance, enabling sustainable adoption across markets and brands. Key Responsibilities and Activities Machine Learning Model Development Design, develop, validate and maintain machine learning models supporting forecasting, pricing optimisation, promotion optimisation and commercial analytics use cases. Develop scalable forecasting models across demand, sales and commercial planning processes, supporting improved business planning, S&OP effectiveness and decision quality. Build optimisation engines and analytical models that support Revenue Growth Management decisions, including trade investment, promotional effectiveness and net sales performance opportunities. Translate business needs into robust ML technical solutions, balancing accuracy, interpretability, usability and operational feasibility. MLOps, Industrialisation & Productisation Transform analytical prototypes and data science models into scalable, production-ready AI products that can be deployed, monitored and maintained globally. Design and implement MLOps pipelines for automated model training, retraining, deployment, versioning, performance monitoring and lifecycle management. Establish reusable model components, technical patterns and deployment accelerators that can be replicated across markets, brands and business functions. Ensure production ML solutions are reliable, maintainable and aligned with enterprise architecture, security and operational standards. RGM (Revenue Growth Management) & Forecasting Enablement Support the industrialisation of AI-enabled RGM use cases, including price optimisation, promotion optimisation, trade investment decision support and predictive commercial insights. Improve forecast accuracy by embedding advanced ML models in commercial and planning workflows. Enable predictive and prescriptive analytics capabilities that move KPI usage beyond retrospective reporting and towards forward-looking decision support. Collaborate with business teams to ensure ML solutions are adopted and embedded into relevant planning, commercial and performance management processes AI Governance, Explainability & Model Monitoring Support model explainability, transparency and governance requirements, ensuring business stakeholders can understand and trust model outputs. Monitor model quality, drift, performance and adoption, recommending improvements and corrective actions when required. Collaborate with Data Governance, Security, Enterprise Architecture and business stakeholders to ensure ML solutions comply with company standards and responsible AI principles. Maintain documentation, controls and operating practices required for production-grade ML solutions. Enterprise Integration & Automation Integrate ML outputs into enterprise platforms, business workflows, dashboards and decision-support tools. Collaborate with Data Platform, Data Engineering and Application teams to ensure the availability, quality and scalability of the data pipelines required by ML products. Support automation of repetitive analytical activities, enabling business teams to focus on higher-value interpretation, planning and decision-making. Contribute to Agentic AI scenarios where forecasting, optimisation and business workflows are connected through intelligent assistants and agents. Stakeholder Partnership & Continuous Improvement Partner with business stakeholders to prioritise ML use cases based on value, feasibility, scalability and strategic relevance. Provide technical guidance to data scientists, analysts and business teams on ML engineering, deployment and maintainability considerations. Stay informed about emerging machine learning, optimisation and MLOps technologies, assessing relevance for Campari Group priorities. Contribute to building sustainable internal AI capabilities and reducing long-term dependency on external consultants and contractors. Required Skills and Experience Experience & Background 7+ years of experience in Machine Learning Engineering, Data Science, Data Engineering, Software Engineering or similar AI engineering roles. Proven experience developing, deploying and maintaining machine learning models in production environments. Experience with forecasting, optimisation, predictive modelling or commercial analytics use cases is strongly preferred. Experience operating in complex, international business environments and working with cross-functional stakeholders. Experience translating analytical prototypes into scalable products and reusable capabilities. Machine Learning & Forecasting Skills Strong knowledge of supervised and unsupervised machine learning techniques, time-series forecasting, optimisation methods and model evaluation approaches. Experience building demand forecasting, sales forecasting, pricing, promotion optimisation or decision-support models. Understanding of model explainability, feature engineering, model monitoring and model performance management. Ability to balance model accuracy with business interpretability, scalability and operational adoption. Knowledge of AI-enabled decision-support capabilities in commercial, planning or supply chain contexts is considered a plus. Technical & MLOps Skills Strong programming skills in Python and familiarity with common ML libraries and frameworks. Hands-on experience with cloud-based AI and data platforms, preferably Azure AI Services, Azure Machine Learning, Databricks or equivalent technologies. Experience designing MLOps pipelines, CI/CD workflows, mod
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
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