Job Title

Must-Have Skills:Advanced proficiency in R (production-level machine learning/statistical models)Hands-on experience with Microsoft and MLOps (model deployment, endpoint creation, testing, optimization, and operationalization)Experience refactoring and modernizing legacy machine learning algorithms for cloud deploymentNice-to-Have Skills:Python proficiency (preferred for translating/refactoring R-based models and aligning with the broader development teams technical stack)Experience with model validation, testing, and performance optimizationExperience working within the broader Azure ecosystem (data pipelines, cloud integrations, model operationalization)Role History:Net newProject Details / Team Environment / Additional Comments:CLIENT is hiring a Machine Learning Specialist to support the migration of existing animal-data algorithms from legacy/on-premise R environments into Microsoft.These models analyze animal behavioural and activity data to generate predictive outputs related to animal health, feeding patterns, and behavioural deviations.The successful candidate will own the migration and refactor of these models into Azure ML, validate model integrity and performance, and support the TAC development team during downstream integration and testing.This role is focused on delivering production-ready machine learning models to Azure ML endpoints, which the TAC development team will integrate into the broader product stack.The candidate will collaborate with TAC developers on technical considerations related to deployment, integration, and model behaviour but will not be responsible for building the main application stack.The engagement will start with heavier hands-on development and migration work and transition into lighter advisory and support responsibilities during later integration phases.Contract is remote and open to candidates located anywhere in Canada or broader North American time zones (PTET).DescriptionOverview:The Machine Learning Specialist will support a modernization initiative involving the migration of multiple machine learning models built in R into Microsoft.These models process animal behavioural and activity data and support predictive decision-making across the Animal Agriculture business.This role will focus on understanding legacy models, refactoring and modernizing them for Azure ML deployment, validating model performance, and supporting technical integration planning alongside the TAC development team.The successful candidate will be responsible for operationalizing models into Azure ML endpoints that can be consumed by the broader product stack.Responsibilities:Review and understand legacy R-based machine learning algorithmsRefactor and modernize existing models for Azure ML deploymentDeploy machine learning models to Azure ML endpointsValidate model performance and output integrity post-migrationOptimize model performance, scalability, and maintainability in cloud environmentsSupport testing during deployment and integration phasesAdvise TAC development teams on model deployment and integration considerationsTroubleshoot model issues during migration, testing, and deploymentImplement MLOps best practices for model deployment, monitoring, and operationalizationRequirements:5+ years of experience in Machine Learning, Data Science, or Statistical ModelingAdvanced R programming experience in production environmentsHands-on experience with Microsoft and MLOps workflowsExperience deploying machine learning models to production endpointsExperience modernizing or refactoring legacy machine learning modelsStrong understanding of model deployment workflows and operationalizationStrong debugging, testing, and troubleshooting capabilitiesExperience working with predictive models in production environmentsNice to Have:Python proficiencyAzure ecosystem exposureModel performance optimization experienceAgricultural analytics or animal behavioural analytics experienceAdditional Details:34 month contractEstimated full-time hours to start (higher utilization in initial phases)Heavier first 68 weeksLighter advisory/support in later phases (part-time hours depending on project needs)Remote (Canada or North American time zones)Start ASAP (within 3 weeks)