DevOps / MLOps Engineer Offline

Requirements:

• Proven experience in deploying and monitoring machine learning models in production environments.
• 5+ years of experience working with Docker, Kubernetes, Helm, and CI/CD pipelines and best practices.
• 5+ years of experience with observability tools such as Prometheus, Thanos, and Grafana.
• Familiarity with model monitoring tools such as Arize, Evidently AI, and Alibi Detect.
• Experience with A/B testing and service mesh software such as Istio.
• Proficiency in using platforms like Kubeflow and OpenDataHub for model deployment and management.
• Strong understanding of infrastructure monitoring and observability best practices.
• Excellent problem-solving skills and the ability to troubleshoot complex issues.
• Experience with cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure.
• Knowledge of scripting and automation tools (e.g., Bash, Python)

Responsibilities:

• Deploy and manage machine learning models on Kubernetes clusters.
• Develop robust data pipelines for training, inference, and analytics purposes.
• Monitor and manage cluster infrastructure using Prometheus, Thanos, and other observability tools.
• Implement and maintain model observability frameworks using tools like Arize.
• Implement A/B testing strategies and software, such as Istio, to evaluate model performance and reliability.
• Develop and maintain dashboards and alerting systems to track model performance and data quality metrics.
• Collaborate with data scientists and ML engineers to ensure models are robustly monitored, and issues are quickly identified and resolved.
• Ensure the scalability and reliability of model deployment pipelines using Kubernetes.
• Stay up to date with the latest advancements in model observability and infrastructure monitoring technologies.