MLOps Engineer

Key Responsibilities Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.Automate model training, testing, deployment, and monitoring in cloud environments (e.g., Google Cloud Platform, AWS, Azure).Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)Collaborate with engin

Key ResponsibilitiesDesign, build, and orchestrate scalabledata pipelinesusing Azure-native services.Develop and optimizebatch and near real-time data processing solutionsusing Spark and Databricks.Implement and manageenterprise-grade Lakehouse architectures(Medallion architecture).Performdata processing, validation, and automationusing Python (PySpark, Pandas, NumPy).Optimize performance of large-scale data systems, includingSpark tuning and query optimization.Work withCDC (Change Data Capture)

Description: Remote Our client seeks a Python-heavy MLOps engineer to own coding, debugging, and maintenance for a fast-evolving pilot ML Python application. The role focuses on backend Python and SQL in a lightly documented codebase, adjacent to machine learning without designing models. You will troubleshoot issues across code, data, and logic, implement business rule changes, and enhance logging to support downstream reporting. This work enables the senior data scientist to concentrate on m

Due to the nature of the work, Our Client is unable to work with candidates on H1-B visa or EAD. Our Client is looking for someone who blends data science + data engieering. Youll architect and build pipelines in Databricks, design data lake systems, and dig into data to find trends, outliers, and actual causal insights. As an Applied Data Scientist, you will bridge the gap between raw data and actionable insights. You are responsible for building robust data pipelines in Databricks and applyin