About this role
Own the end-to-end lifecycle of machine learning models in production at CUBE, building infrastructure and operational discipline for proprietary ML models and large language model integrations. Responsibilities include designing and automating ML pipelines with Azure tooling, model deployment and serving, monitoring and observability, LLM provider governance, prompt/version management, experiment tracking, and driving platform reliability and cost efficiency.
Skills for this role
MLOpsMachine LearningAzure AI FoundryAzure Machine LearningAzureML pipelinesModel deploymentContainerised model endpointsModel servingModel versioningTraffic managementRollback mechanismsMonitoringObservabilityModel performance monitoringData drift detectionAlerting frameworksLLM provider managementOpenAIAzure OpenAIAnthropicAPI access managementToken consumption trackingCost optimisationLLM gatewayPrompt versioningLangSmithHeliconeTracingEvaluation tooling for LLMs and prompts