Job Summary
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.
Job Description : • Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn.\\\\r\\\\n• Strong understanding of MLOps best practices and tools, including Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.\\\\r\\\\n• Experience working with cloud platforms, especially GCP.\\\\r\\\\n• Knowledge of data processing, ETL, and feature engineering techniques.\\\\r\\\\n• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.\\\\r\\\\n
Key Responsibilities
Job Responsibilities : • 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.
Skill Requirements
• Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.
Other Requirements
• 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.