• Work in agile pods to design and build cloud hosted, ML products with automated pipelines that run, monitor, and retrain ML Models
• Design AI/ML apps and implement automated model and pipeline adaption and validation working closely with data scientists and data engineers
• Support the full MLOps life cycle of new and existing ML applications (e.g., new releases, change management, monitoring and troubleshooting).
• Work as ML systems architecture design SME (e.g., develop and maintain enterprise standards, user guides, release notes, FAQs)
• Build processes supporting seamless ML integrations (e.g., app monitoring, troubleshooting, life cycle management and customer support)
• Maintain effective relationships with application userbase to develop education and communication content as per life cycle events
• Research and gain expertise on emerging tools and technologies. An enthusiasm to ask questions and try and learn new things is essential

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