Azure ML Ops Consultant

Qualifications

• Work 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 etc
• Hands-on experience with cloud analytics services (Primarily Azure)
• Infrastructure (Server, Storage, and Database) discovery, design, build, and migration experience
• Experience in any of Messaging platforms (Kafka, Azure EventHub, Iot Hub, etc
• Experience in Kubernetes and Microservices
• Knowledge of various database technologies - SQL, NoSQL, Blob, file system, object store etc
• Create and develop CI/CD Pipelines that allow for controlled and continuous enhancement of existing work and new features during both development and production phases
• Experience supporting and working with cross-functional teams in an agile environment
• Experience in agile product development
• Experience in the operationalization of Data Science projects (MLOPs) in Azure

Responsibilities

• The 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 business
• Provide data science expertise for AI products, programs across business units within KC
• Work with teams to design and build cloud based automated pipeline that run, monitor and retrain AI/ML models using agile methodologies
• Have 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 models
• Enhance and improve the code deployment and model monitoring frameworks and project operations documentation
• Lead the scalable implementation of solution for AI model governance and model behavior analytics
• Support 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 requirements
• Create & evolve data & analytics technology roadmap, to align with continuously evolving business needs including overall architecture, capabilities, platforms, tools & governing processes
• Create, maintain & communicate positioning/go-forward strategies for data & analytic capabilities/tools
• Own strategic technology relationships with technology vendors & external communities/partners
• Help define/improve best practices, guidelines & integration with other enterprise solutions