At AgileEngine, we create award-winning software for Fortune 500 brands and startups across various industries. We are leaders in application development and AI/ML, with a people-first culture recognized by multiple Best Place to Work awards. If you're seeking growth, impact, and a caring team, this role may be the right fit for you. Key Responsibilities - Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores. - Design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production. - Develop calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle. - Design and optimize reinforcement learning (RL) orchestration pipelines for real-time, low-latency environments. - Create frameworks for training, retraining, and validating ML models to enable efficient experimentation and deployment. - Implement best practices for CI/CD to streamline deployment and monitoring of ML models, integrating with Databricks workflows and Git-based systems. - Collaborate with ML Scientists to ship, deploy, and maintain models. Requirements - 3+ years of experience in MLOps, ML Engineering, Data Engineering, or related roles managing ML workflows in production. - 5+ years of experience using Python. - Proficiency with Databricks, Apache Spark, MLflow, Unity Catalog, and feature stores. - Familiarity with ML lifecycle tools like MLflow, Kubeflow, and Airflow. - Strong knowledge of Git workflows, CI/CD practices, and tools such as GitLab. - Understanding of model performance monitoring, drift detection, and retraining workflows.