MLOps Engineer at PatSnap

The MLOps Engineer will design, build, and maintain scalable ML model deployment pipelines for real-time and batch inference, manage and optimize cloud-based ML infrastructure to ensure high availability and cost efficiency, implement monitoring, logging, and alerting systems for ML models in production, automate model training, evaluation, and deployment processes using CI/CD pipelines, ensure compliance with MLOps best practices, collaborate with data scientists and developers to streamline model transitions, optimize model serving infrastructure using Kubernetes and serverless technologies, improve data pipelines for feature engineering, and research and implement tools for scalable AI development, such as Retrieval-Augmented Generation (RAG) and agent-based applications.