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• Minimum of 5 years of professional experience in MLOps or a related field.
• Proven experience deploying and managing machine learning models in production environments.
• Proficiency in scripting languages (e.g., Python) and relevant MLOps tools (e.g., TensorFlow Extended, Kubeflow, MLflow).
• Experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
• Strong knowledge of cloud platforms (AWS, GCP, or Azure) and their machine-learning services.
• Demonstrated experience implementing automated testing, validation, and deployment processes for machine learning models.

• Python
• Azure / AWS / GCP
• Grafana / Prometheus
• SQL

• Develop and implement a comprehensive MLOps strategy, ensuring the seamless integration of machine learning models into our production environment.
• Design, build, and maintain end-to-end machine learning pipelines, encompassing data preprocessing, model training, deployment, and monitoring.
• Collaborate with cross-functional teams to design, deploy, and manage scalable infrastructure for machine learning workloads. Utilise containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP, or Azure).
• Implement and manage CI/CD pipelines for machine learning models, enabling automated testing, validation, and deployment.
• Establish robust monitoring and logging systems to track the performance of machine learning models in production, ensuring timely detection of anomalies and potential issues.
• Work closely with data scientists, software engineers, and other stakeholders to understand model requirements, deployment needs, and data dependencies.
• Implement security best practices for machine learning systems and ensure compliance with relevant regulations and standards.

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