QualificationsWork across the full stack is a must have requirement, moving seamlessly between programming languages and technologies: Python, PySpark, MLFlow, Azure Databricks, ADLS, Snowflake, Azure DevOps, API, Kuberenetes etcHands-on experience with cloud analytics services (Primarily Azure)Infrastructure (Server, Storage, and Database) discovery, design, build, and migration experienceExperience in any of Messaging platforms (Kafka, Azure EventHub, Iot Hub, etcExperience in Kubernetes and MicroservicesKnowledge of various database technologies - SQL, NoSQL, Blob, file system, object store etcCreate and develop CI/CD Pipelines that allow for controlled and continuous enhancement of existing work and new features during both development and production phasesExperience supporting and working with cross-functional teams in an agile environmentExperience in agile product developmentExperience in the operationalization of Data Science projects (MLOPs) in AzureResponsibilitiesThe MLOps Engineer will work closely with the data scientists working on AI products and solutions across various K-C business units to take the AI models developed and operationalize and own the life cycle management of the models in production for continuous value creation The role will lead our strategic effort of AI life cycle management capabilities such as continuous deployment, model drift and behavior monitoring, model governance, retraining in alignment with business KPIs for continuous value creation for businessProvide data science expertise for AI products, programs across business units within KCWork with teams to design and build cloud based automated pipeline that run, monitor and retrain AI/ML models using agile methodologiesHave a strategic perspective of how several ML solutions come together against a set of business objectives, product and AI strategy leading to optimal operations of the modelsEnhance and improve the code deployment and model monitoring frameworks and project operations documentationLead the scalable implementation of solution for AI model governance and model behavior analyticsSupport life cycle management of AI models (eg, new releases, change management, monitoring, retraining, and troubleshooting)Compare solution alternatives across both technical and business parameters which support the define cost and service requirementsCreate & evolve data & analytics technology roadmap, to align with continuously evolving business needs including overall architecture, capabilities, platforms, tools & governing processesCreate, maintain & communicate positioning/go-forward strategies for data & analytic capabilities/toolsOwn strategic technology relationships with technology vendors & external communities/partnersHelp define/improve best practices, guidelines & integration with other enterprise solutions