Machine Learning Engineer Opportunity We are seeking a skilled engineer to join our team. As an ML Ops Engineer, you will be responsible for building and maintaining scalable ML infrastructure on Databricks, designing and implementing frameworks for detecting data and model drift, and developing model calibration frameworks. You will work closely with scientists to deploy and maintain models, and build tools for performance monitoring, operational analytics, and drift mitigation. Key Requirements - At least 3 years of experience in MLOps, ML Engineering, Data Engineering or related roles, focusing on deploying and managing workflows in production environments; - 5+ years of experience using Python; - Proficient in using Databricks (2-3 years), Apache Spark, ML Flow, 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 or similar; - Strong understanding of performance monitoring, drift detection, and retraining workflows; Desirable Skills - Experience with deployment and management of machine learning models; - Knowledge of data engineering concepts and principles; - Familiarity with cloud-based platforms such as AWS or GCP; - Understanding of software development life cycles and agile methodologies; This role is ideal for someone who enjoys working with complex data sets and has a passion for innovation.