Seven (7) years in any occupation with experience working in data science, machine learning or AI must include: Seven (7) years of experience writing production-quality object-oriented code (Python or C++) and working with ML frameworks such as scikit-learn, TensorFlow, or similar, including five (5) years of experience building low-latency or real-time ML systems; Seven (7) years of experience designing, building, and deploying large-scale recommender or decisioning systems, including techniques such as ranking, clustering, and other unsupervised learning methods; Seven (7) years of experience building and deploying production ML systems on cloud platforms (Google Cloud, Azure, or AWS); Six (6) years of experience applying optimization techniques in production environments, including large-scale ML or decisioning systems such as operational research, offer optimization, or revenue maximization; Five (5) years of experience working in the Retail Industry; Five (5) years of experience building scalable, production-grade real-time inference services using Docker, with CI/CD and monitoring; Three (3) years of experience as Sr AI or ML Engineer with demonstrated ability to drive technical direction and own AI tech solutions; Three (3) years of experience designing and developing reinforcement learning models in real-world or production settings; Two (2) years of experience working with large language models (LLMs); Two (2) years of experience building or integrating large-scale batch ML pipelines using cloud-native event-driven systems (e.g., Pub/Sub) and containerized execution (e.g., GKE); Two (2) years of experience developing agentic solutions within Databricks and Google Gemini Enterprise leveraging ADK, LangGraph – including multi-agent orchestration patterns; Two (2) years of experience partnering with AI Architects and Product team to translate requirements into agentic design patterns, defining orchestration and automation agentic behavior. Experience must also include: Experience working with large-scale data processing and analytics platforms, including Apache Spark and Databricks, for feature engineering, model training, and data pipelines; Experience with relational databases including Postgres, Bigquery and MySQL; Document databases including MongoDB; and low-latency databases including BigTable; and Experience building and delivering complex ML solutions from inception to launch.