MLOps Engineer

Design, implement, and maintain scalable and reliable MLOps pipelines. Develop and deploy machine learning models using Python. Manage containerized applications using Kubernetes. Implement and manage CI/CD pipelines to automate the deployment process. Collaborate with data scientists and software engineers to integrate machine learning models into production environments. Utilize GitHub for version control and collaboration on code repositories. Leverage Argo for workflow automation and orchestration. Monitor and optimize the performance of deployed models and infrastructure. Troubleshoot and resolve issues related to model deployment and infrastructure. Stay up-to-date with the latest trends and best practices in MLOps and machine learning. Required Qualifications: Bachelor's degree in Computer Science, Engineering, or a related field. Proven experience as an MLOps Engineer or in a similar role. Proficiency in Python programming. Hands-on experience with Kubernetes for container orchestration. Strong understanding of code deployment and CI/CD processes. Experience with GitHub for version control. Familiarity with Argo for workflow automation. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills. Preferred Qualifications: Master's degree in Computer Science, Engineering, or a related field. Experience with cloud platforms such as AWS, GCP, or Azure. Knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch). Experience with monitoring tools and practices for machine learning models. Understanding of data engineering and data pipeline concepts.