The Demand Planning team within Client s FinOps organization is a forward-thinking, data-driven group responsible for capacity demand planning, forecasting, cost measurement, reporting, and analytics for Client's global infrastructure. Our mission is to deliver industry-leading experiences by accurately forecasting customer demand, optimizing capacity planning, and enabling hybrid cloud strategies. We are evolving our forecasting models to incorporate multi-dimensional metrics and AI/ML techniques, ensuring scalability and precision as business needs grow. This role sits at the intersection of Machine Learning, AI innovation, and cloud infrastructure planning, driving next-generation forecasting solutions. Role Overview As a Machine Learning Engineer, you will design and implement advanced AI/ML-driven forecasting models to predict infrastructure demand across hybrid environments. You ll collaborate with data scientists, engineers, and capacity planners to build scalable solutions that integrate time-series modeling, deep learning architectures, and hybrid AI techniques. Your work will directly influence capacity roadmaps, customer segmentation, and forecast accuracy, enabling Client to optimize global rack, server, and data center utilization. Key Responsibilities Develop and deploy AI/ML forecasting models for long-range demand and supply planning. Enhance accuracy by incorporating multi-metric inputs and hybrid cloud strategies. Apply advanced techniques: ARIMA, Bayesian models, RNN, LSTM, and hybrid AI approaches. Build POCs parallel to existing models to validate AI-driven forecasting improvements. Segment customers using ML for tailored capacity solutions. Run scenario-based forecasts to optimize scaling and utilization across service tiers. Collaborate with hardware, infrastructure, and cloud analytics teams to create capacity roadmaps. Automate workflows for forecasting, reporting, and analytics pipelines. Own the end-to-end delivery of ML solutions: design, implementation, testing, and deployment. Provide insights and reports to leadership on forecast variability and model performance. | 
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