AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and startups across 17+ industries. We are leaders in application development and AI/ML, with a people-first culture recognized by multiple Best Place to Work awards. Our team is looking for a talented ML Ops Engineer to join us on our mission to drive innovation and growth. Key Responsibilities: - Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores for model development and deployment. - 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 for transparency and reproducibility across the ML lifecycle. - Design and optimize reinforcement learning orchestration pipelines, including Contextual Bandits, 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 systems. - Collaborate closely with ML Scientists to ship, deploy, and maintain models. Requirements: - 3+ years of experience in MLOps, ML Engineering, Data Engineering, or related roles focusing on ML workflows in production. - 5+ years of experience with Python. - Proficiency with Databricks (2-3 years), Apache Spark, MLflow, Unity Catalog, and feature stores. - Familiarity with ML lifecycle tools such as MLflow, Kubeflow, and Airflow. - Strong knowledge of Git workflows, CI/CD practices, and tools like GitLab. - Understanding of model performance monitoring, drift detection, and retraining workflows.