Principal DevOps Engineer - Machine Learning Data Engineering

Your work days are brighter here.At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. A culture driven by our value of putting our people first is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is essential for success in business. We look after our people, communities, and the planet while still being profitable. Feel encouraged to shine; you don’t need to hide who you are.About The TeamThis is an opportunity to be part of a growth team focused on ML DevOps and ML Ops. We build ML capabilities into our products, and you will be building part of the next generation of Workday technology. As a DevOps engineer, you will help develop ML powered features and experiences for every user across our HR & Talent product portfolio.About The RoleIn this role, you would:Work with multi-functional teams to deliver scalable, secure and reliable solutions.Effectively engage with data scientists, ML engineers, PMs, and architects in requirements elaboration and drive technical solutions.Own and develop features from end to end including infrastructure as code.Design and build solutions for efficient organization, storage and retrieval of data to enable substantial scale.Build systems and dashboards to monitor service & ML health.Lead in architecture reviews, code reviews and technology evaluation.Research, evaluate, prototype and drive adoption of new ML tools with reliability and scale in mind.About YouBasic Qualifications6 or more years of validated industry experience.Bachelor’s and/or Master’s degree, preferably in CS, or equivalent experience.Design, implement, and maintain robust DevOps pipelines for deploying, monitoring, and scaling machine learning development and data engineering.Stay abreast of industry trends and emerging technologies, providing recommendations for continuous improvement of our DevOps and machine learning practices.Troubleshoot and resolve performance bottlenecks, system outages, and other operational issues in collaboration with the ML engineering teams.Optimize public cloud-based infrastructure (AWS, Azure, or GCP) to support the computational requirements of machine learning workloads.Implement and manage CI/CD workflows to automate testing, integration, and delivery of machine learning components.Develop and maintain monitoring and alerting systems for proactively identifying and addressing issues within the machine learning infrastructure.Ensure the security and compliance of machine learning platforms, implementing best practices for encryption, data protection, and access controls.Experience in managing relevant tools like Databricks and Sagemaker to perform efficient computation and management of large scale data lakes.Experience in supporting your work in production.6 or more years of DevOps or programming experience preferably in Python, Java or Scala.Other QualificationsImplementation and operation of distributed systems.Experience of data and/or ML systems with ability to think across layers of the stack.Experience with Databricks, Sagemaker, & Apache-Spark.Experience in leading or mentoring other team members.Workday Pay Transparency StatementThe annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things.Our Approach to Flexible WorkWith Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role).

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