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.