We are looking for an experienced MLOps Engineer with a strong background in the banking domain to join our team. This role involves working with data scientists, software engineers, and business analysts to deploy, monitor, and scale machine learning models in production, with a focus on highly regulated banking environments.

• Develop and implement automated pipelines for deploying machine learning models into production.
• Work on containerization and orchestration of models using Docker, Kubernetes, and other relevant tools.
• Ensure continuous integration and continuous deployment (CI/CD) of machine learning models.
• Collaborate with data scientists to take models from development into production, optimizing for real-time performance.
• Set up monitoring and logging systems to track model performance and ensure proper functionality in production.
• Continuously evaluate model performance and retrain models when necessary based on financial trends and business needs.
• Manage cloud infrastructure (AWS, GCP, Azure) to deploy, monitor, and scale ML models.
• Ensure the security, performance, and compliance of deployed ML models in alignment with banking regulations.
• Implement data privacy measures and ensure secure handling of sensitive customer data during the ML model lifecycle.
• Optimize machine learning algorithms for high-volume, low-latency financial transactions.
• Maintain documentation of MLOps processes, workflows, and model performance.
• Create detailed reports and dashboards to track model accuracy and performance metrics.