
Machine Learning Operations Pipeline Engineer
1 day ago
MLOps Lead Engineers are responsible for building and maintaining robust machine learning pipelines to ensure efficient model deployment, monitoring, and lifecycle management within a cloud-based environment.
Key Responsibilities- Develop extensive expertise in MLOps specifically with Google Cloud Platform (GCP) and Vertex AI.
- Understand model performance drift detection and GPU accelerators.
- Build and maintain scalable MLOps pipelines in GCP Vertex AI for end-to-end machine learning workflows.
- Manage the full MLOps lifecycle from data preprocessing, model training, and deployment to model monitoring and drift detection.
- Implement real-time model monitoring and drift detection to ensure optimal model performance over time.
- Utilize CICD containerization, and orchestration tools such as Jenkins, GitHub Pipelines, Docker, Kubernetes, and OpenShift.
- Optimize model training and inference processes using GPU accelerators and CUDA.
- Collaborate with cross-functional teams to automate and streamline machine learning model deployment and monitoring.
- Set up infrastructure for continuous training, testing, and deployment of machine learning models.
- Python 3.10 with libraries such as pandas, NumPy, and TensorFlow.
- Experience in managing scalability, security, and high availability in all machine learning operations.
- Deep understanding of model performance drift detection and GPU accelerators.
- Collaborative and dynamic work environment.
- Opportunities for professional growth and development.
As an MLOps Lead Engineer at [Company Name], you will be part of a talented team working on cutting-edge projects that drive business innovation.
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