We are seeking a highly skilled MLOps Engineer to join our team. As a key member of our organization, you will play a crucial role in building and maintaining scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores. Key Responsibilities: - 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. In this role, you will have the opportunity to work on challenging projects, collaborate with experienced professionals, and contribute to the development of cutting-edge AI/ML solutions. If you're passionate about machine learning and eager to take your career to the next level, we encourage you to apply.