Job Description As a highly skilled professional, you will be responsible for developing and maintaining scalable machine learning (ML) infrastructure on Databricks. You will design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production. You will also develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle. Additionally, you will create automated frameworks for training, retraining, and validating ML models, enabling efficient experimentation and deployment. Furthermore, you will work closely with ML scientists to ship, deploy, and maintain models, as well as build tools for model performance monitoring, operational analytics, and drift mitigation. 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 knowledge of Git workflows, CI/CD practices, and tools like GitLab or similar - Strong understanding of model performance monitoring, drift detection, and retraining workflows Benefits - Professional growth through mentorship, TechTalks, and personalized growth roadmaps - Competitive compensation package - Selection of exciting projects with modern solutions development and top-tier clients - Flextime for optimal work-life balance