senior engineer, DPS AI/ML Platforms – ST

2 weeks ago

International WA United States Starbucks Full time

At our core, we believe technology is a key enabler for growth and is required for business success in the future. We believe Starbucks must advance its technology resources and think long term to innovate and deliver maximum value for our partners, customers and shareholders. We believe it takes collaboration and partnership to deliver results and we will work across the company to enable sustainable business capabilities- not just fulfill immediate needs. The Enterprise Data and Analytics Platform team is in the middle of an exciting transformation in building out On Demand platforms to enable Advanced Analytics and Machine Learning at scale to power multiple Data , AI, ML initiatives. As such, your role will be key contributor to building and securing next generation Data, Analytics and Machine Learning capabilities into Enterprise Data and Analytics Platform. Job Summary and Mission As a Starbucks senior engineer, your core role will be contributing to the development and operations of our Enterprise AI/ML Platform. In this role you will be focused on the operations and management of machine learning models, algorithms, and processes. As an MLOps engineer you will be responsible for building set of management techniques for the deep learning or production ML lifecycle hosted on enterprise Data Infrastructure, ML Training Infrastructure, ML Model Deployment Infrastructure and Frameworks that are provisioned on Demand, hardened for Starbucks Security standards and Digital scale. You will bring in solid skills to act as an Individual Contributor involving Building and enhancing SRE capabilities for producitonalizing AI/ML models. Azure Cloud Automation using Python or Powershell and ARM Demonstrated expertise in Kubernetes Cluster Design, administration and operations Demonstrated expertise in CICD tooling such as Jenkins, Azure Dev Ops Demonstrated expertise in Observability and Proactive Alerting/Monitoring platforms like Datadog, PagerDuty and ServiceNow. Exposure to Azure services like Azure Databricks and Azure Machine Learning desirable This role requires your A-Game: Deep technical proficiency in enterprise-scale cloud native ML applications required. So, if you believe, like we do, that a cup of coffee can change a life and change our world, come check us out and help us deliver that same amazing experience to our customers around the globe. Models and acts in accordance with Starbucks guiding principles. Summary of Key Responsibilities: Plan, implement and support core infrastructure with an overall objective to improve the scalability, reliability, performance, and availability Deep understanding of the AI/ML Model Lifecycle Management. Demonstrate deep understanding of the AI/ML Platform infrastructure and cloud architecture. Design, develop, troubleshoot, debug, evaluate, modify, deploy and document application, system, or infrastructure software. Provide integration and deployment tools and script. Lead Production Deployments : hotfixes, weekly releases and new model deployments. Ensures system performance, uptime and support levels meet or exceed SLAs. Continuously improve software engineering practices, maintain and enhance operational handbooks for all production models. Ability to perform Root Cause Analysis for production issues of ML Models. Ensuring platform and (modelappdata) health monitoring and observability framework modules/utils are developed and published for integration in ML Pipelines. Develop and enable self-serve model observability(model monitoring, drift monitoring , perf SLA’s) dashboards for platform and business stakeholders. Develop and maintain Data Quality and Data observability framework for integrations in ML pipelines. Build , develop and standardize integrations needed for Model Governance framework implementations. Demonstrate ability to collaborate with a geo-graphically spread vendor team supporting MLOps. Bring a passion to stay on top of tech trends, experiment with and learn new technologies, participate in internal & external technology communities, and mentor other members of the engineering community

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