Job Description:

• Design, implement, and maintain robust MLOps pipelines for the deployment and monitoring of ML/AI models.
• Collaborate with data scientists and ML engineers to automate model training, validation, deployment, and scaling processes.
• Build and manage CI/CD pipelines using tools like Jenkins for seamless ML workflows.
• Containerize ML applications using Docker and orchestrate deployments using Kubernetes.
• Manage and optimize infrastructure for ML workloads using microservices and serverless technologies.
• Ensure reliable version control and environment consistency using GitOps tools such as ArgoCD or Flux.
• Monitor the health, performance, and accuracy of deployed ML models and systems in production.
• Implement security best practices and compliance standards in ML/AI deployments.
• Contribute to the development of internal MLOps frameworks, tools, and reusable components.
• Support model retraining, rollback strategies, and lifecycle management to ensure model integrity.
• Work in an Agile/Scrum team to deliver iterative improvements and align MLOps solutions with business objectives.