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2 days ago
Our organization is seeking a skilled Machine Learning Operations (MLOps) Engineer to oversee the development and deployment of machine learning models in our cloud-based environment. As an MLOps Engineer, you will be responsible for building and maintaining robust machine learning pipelines, ensuring efficient model deployment, monitoring, and lifecycle management.
Key Responsibilities:- Build and Maintain Scalable MLOps Pipelines: Design and implement scalable MLOps pipelines in Google Cloud Platform (GCP) Vertex AI for end-to-end machine learning workflows.
- Manage the Full MLOps Lifecycle: Oversee the full MLOps lifecycle, from data preprocessing to model training, deployment, monitoring, and drift detection.
- Implement Real-time Model Monitoring and Drift Detection: Utilize tools and techniques to monitor and detect model performance drift, ensuring optimal model performance over time.
- Collaborate with Cross-functional Teams: Work with cross-functional teams to automate and streamline machine learning model deployment and monitoring.
- Expertise in MLOps: Extensive experience in MLOps, specifically with GCP and Vertex AI.
- Programming Languages: Proficient in Python 3.10, with libraries such as pandas, NumPy, and TensorFlow.
- Containerization and Orchestration: Experience with containerization using Docker and orchestration using Kubernetes or OpenShift.
- GPU Accelerators: Knowledge of GPU accelerators and CUDA for optimizing model training and inference processes.
- Opportunity to Work on Cutting-edge Projects: Contribute to the development of innovative machine learning projects.
- Professional Growth: Opportunities for professional growth and development in a dynamic and fast-paced environment.
- Collaborative Team Environment: Work with a collaborative team of experienced professionals.
- Infrastructure Setup: Set up infrastructure for continuous training, testing, and deployment of machine learning models.
- Scalability, Security, and High Availability: Ensure scalability, security, and high availability in all machine learning operations by implementing best practices in MLOps.
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