Machine Learning Engineer Role We are seeking a skilled Machine Learning Engineer to join our team and contribute to the development of innovative machine learning solutions. The ideal candidate will have expertise in deploying and managing machine learning workflows in production environments, as well as experience with Python programming. The successful applicant will work closely with our data scientists to design, develop, and maintain scalable machine learning infrastructure on Databricks, leveraging Unity Catalog and feature stores to support model development and deployment. Key responsibilities include: - Designing and implementing frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production; - Developing model calibration frameworks and establishing versioning practices to maintain transparency and reproducibility across the ML lifecycle; - Designing and optimizing reinforcement learning (RL) orchestration pipelines for real-time execution in low-latency environments; - Creating automated frameworks for training, retraining, and validating ML models enabling efficient experimentation and deployment; - Implementing CI / CD best practices to streamline the deployment and monitoring of ML models, integrating with Databricks workflows and Git-based version control systems; Requirements for this role include: - At least 3 years of experience in MLOps, ML Engineering, Data Engineering or related fields, with a focus on deploying and managing ML workflows in production environments; - 5+ years of experience using Python programming language; - Proficiency 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; In return for your expertise, we offer a competitive compensation package and opportunities for professional growth and development.