About ML Ops Engineering We are seeking an experienced machine learning engineer to join our team. As a key member of the engineering department, you will be responsible for building and maintaining scalable infrastructure on Databricks, designing frameworks for detecting data and model drift, and developing calibration frameworks to ensure continuous monitoring and high reliability of ML models in production. Key Responsibilities: - Design and implement frameworks for detecting data and model drift - Develop calibration frameworks and establish versioning practices - Optimize reinforcement learning orchestration pipelines for real-time execution in low-latency environments - Create automated frameworks for training, retraining, and validating ML models - Implement CI/CD best practices to streamline deployment and monitoring of ML models Requirements: - 3+ years of experience in MLOps, ML Engineering, Data Engineering or related roles - 5+ years of experience using Python - Proficient in using Databricks, Apache Spark, ML Flow, Unity Catalog and feature stores - Familiarity with ML lifecycle tools such as MLflow, Kubeflow, and Airflow - Strong understanding of Git workflows, CI/CD practices and tools like GitLab or similar - Upper-Intermediate English level Benefits: - Professional growth: Accelerate your career with mentorship, TechTalks and personalized growth roadmaps - A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands - Flextime: Tailor your schedule for optimal work-life balance