Design, build, and prototype AI agents focused on optimizing key supply chain functions for cloud compute infrastructure (e.g., forecasting, capacity planning, demand sensing, component procurement, logistics, and reverse logistics).
Iterate on ideas, conducting rapid A/B testing and proof-of-concept experiments to validate the technical feasibility and business impact of agent-based solutions.
Create comprehensive documentation, including technical specifications, model architecture, and model maintenance procedures to ensure a seamless and efficient transition of successful prototypes to production engineering teams. Run regression tests in production to validate model outcomes.
Partner closely with Supply Chain Operations, Architects, Finance, and Engineering teams to define requirements, gather feedback, and ensure prototypes align with strategic business objectives and drive user adoption in production.
Minimum qualifications:
Bachelor's degree in Computer Science, Engineering, Mathematics, Operations Research, or a related quantitative field.
2 years of experience in developing and deploying Machine Learning models or AI-driven applications.
Experience with agent-based modeling, predictive modeling, or deep learning architectures.
Experience with prompt engineering.
Preferred qualifications:
Experience in Python and standard data science libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow/PyTorch).
Experience putting data science principles into practice, data quality assessment, statistical modeling, feature engineering, and data visualization.
Experience with frameworks like LangChain or LlamaIndex, to build advanced, multi-step agents.
Experience in using vector and graph databases to power Retrieval-Augmented Generation (RAG) in supply chain processes and systems.
Experience working with cloud computing platforms (e.g., Cloud Computing Platform, Google Cloud Platform) and developing solutions that interact with large-scale data warehouses and databases (SQL, NoSQL).
Familiarity with optimization techniques (linear programming, heuristic search) and simulation modeling.
