MLOps Engineer

Role Overview: You are required to be a highly skilled ML Ops Engineer responsible for designing, building, and managing scalable machine learning pipelines and infrastructure. Your role will involve operationalizing machine learning solutions by ensuring reliability, scalability, governance, and seamless integration with enterprise data platforms. Key Responsibilities: - Design, develop, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. - Build scalable data processing workflows using PySpark on Databricks. - Implement and automate CI/CD pipelines for ML workflows to ensure faster and reliable deployments. - Manage model versioning, experiment tracking, and reproducibility using tools like MLflow. - Deploy ML models into production and enable real-time and batch inference pipelines. - Continuously monitor model performance, data drift, and system health in production environments. - Collaborate with data scientists, data engineers, and DevOps teams to productionize machine learning models. - Implement security, governance, and access controls across ML pipelines and data workflows. - Optimize cloud infrastructure for performance, scalability, and cost efficiency. - Support troubleshooting, root cause analysis, and continuous improvement of ML systems. - Enable automated retraining pipelines and lifecycle management of models. Qualification Required: - Strong programming skills in Python and PySpark. - Good understanding of SQL and large-scale data processing. - Hands-on experience with Databricks. - Experience with ML platforms/tools such as MLflow, SageMaker, or equivalent. - Strong knowledge of CI/CD tools (Jenkins, GitHub Actions, Azure DevOps, etc.). - Experience working with cloud platforms (AWS / Azure / GCP). - Solid understanding of ML lifecycle management, deployment, and monitoring. - Familiarity with Docker and Kubernetes for containerization and orchestration. - Understanding of data pipelines and distributed computing systems. Additional Details: Not provided in the Job Description. Role Overview: You are required to be a highly skilled ML Ops Engineer responsible for designing, building, and managing scalable machine learning pipelines and infrastructure. Your role will involve operationalizing machine learning solutions by ensuring reliability, scalability, governance, and seamless integration with enterprise data platforms. Key Responsibilities: - Design, develop, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. - Build scalable data processing workflows using PySpark on Databricks. - Implement and automate CI/CD pipelines for ML workflows to ensure faster and reliable deployments. - Manage model versioning, experiment tracking, and reproducibility using tools like MLflow. - Deploy ML models into production and enable real-time and batch inference pipelines. - Continuously monitor model performance, data drift, and system health in production environments. - Collaborate with data scientists, data engineers, and DevOps teams to productionize machine learning models. - Implement security, governance, and access controls across ML pipelines and data workflows. - Optimize cloud infrastructure for performance, scalability, and cost efficiency. - Support troubleshooting, root cause analysis, and continuous improvement of ML systems. - Enable automated retraining pipelines and lifecycle management of models. Qualification Required: - Strong programming skills in Python and PySpark. - Good understanding of SQL and large-scale data processing. - Hands-on experience with Databricks. - Experience with ML platforms/tools such as MLflow, SageMaker, or equivalent. - Strong knowledge of CI/CD tools (Jenkins, GitHub Actions, Azure DevOps, etc.). - Experience working with cloud platforms (AWS / Azure / GCP). - Solid understanding of ML lifecycle management, deployment, and monitoring. - Familiarity with Docker and Kubernetes for containerization and orchestration. - Understanding of data pipelines and distributed computing systems. Additional Details: Not provided in the Job Description.