Proficiency in MLOps tooling and practices: containerization (Docker), orchestration (Kubernetes), CI/CD, model versioning, and pipeline tools (e.g., MLflow, Kubeflow, Airflow) with experience with internal Cloud environments such as Gaia and GKP, and creating pipelines to deploy AI/ML models onto these platforms. Master's degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience) and 6+ years of hands-on experience building and deploying ML/AI models in production settings.