Responsibilities:* Develop and deploy end-to-end microservices-based solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance testing.* Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.* Collaborate with Data Scientists to enhance the ML model development process and ensure performance improvements.* Ensure scalability, maintainability, and robustness of deployed machine learning models.* Monitor and troubleshoot ML model performance and infrastructure issues in production (experience with Prometheus and Grafana is valuable).* Support and enhance ML software infrastructure, including CI/CD, data storage, cloud services, security, and system monitoring.* Work with cloud platforms, particularly GCP and Azure, to optimize resource allocation and costs.* Stay up to date with the latest trends and best practices in MLOps.Qualifications:* Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.* 5+ years of experience as a Machine Learning Engineer or in a similar role.* Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn.* Strong understanding of MLOps best practices and tools, including Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.* Experience working with cloud platforms, especially GCP.* Knowledge of data processing, ETL, and feature engineering techniques.* Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.* Excellent communication and interpersonal skills.

MLOps Engineer role