MLOps Engineer role

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.