MLOps Engineer with Google Cloud and Vertex AI (Must Have Retail Industry Background)

MLOps Engineer with Google Cloud and Vertex AI (Must have Retail Industry Background) 6+ Months Contract Remote Position • Bachelor's or Master’s degree in computer science, Engineering, or related field. • Minimum of 4 years of experience in MLOps, with a demonstrated ability to work with various ML platforms. • Strong proficiency in Python and familiarity with data science methodologies. • Experience with cloud technologies, particularly Google Cloud and Vertex AI, and adaptability to technologies like Microsoft Azure or open-source tools. • Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, but open to other systems as needed. • Leverage support for open-source frameworks like TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks via custom containers. • Excellent communication skills, capable of bridging technical and business domains. • We are seeking a highly skilled MLOps Engineer to build, scale, and support our Google Vertex machine learning platform to enable a multi-model serving environment. This role is central to building reusable infrastructure components (i.e., infra as code), model deployment pipelines, and provide a collection of templates that accelerate model deployment, monitoring, and support for domain teams. • This contractor will collaborate closely with data scientists, other MLOps engineers, and product teams to ensure that models are deployed efficiently that are observable, maintainable, and aligned with business goals. • This work will empower domain teams to independently run, support, and monitor their models using platform-provided tools and best practices. • This role is part of a larger ML platform team that supports Kroger’s product recommendation capabilities. • This role will work alongside Data Scientist, Data Engineers, Machine Learning Engineers, and software engineers to build, test, maintain, and support data pipelines, ML Models, and back-end services that make up our product recommender platform.