Job Information
Cargill Generative AI Platform and Infrastructure Engineer in Atlanta, Georgia
Cargill’s size and scale allows us to make a positive impact in the world. Our purpose is to nourish the world in a safe, responsible and sustainable way. We are a family company providing food, ingredients, agricultural solutions and industrial products that are vital for living. We connect farmers with markets so they can prosper. We connect customers with ingredients so they can make meals people love. And we connect families with daily essentials — from eggs to edible oils, salt to skincare, feed to alternative fuel. Our 160,000 colleagues, operating in 70 countries, make essential products that touch billions of lives each day. Join us and reach your higher purpose at Cargill.
Job Purpose and Impact
The Generative AI Platform and Infrastructure Engineer will provide consistent experience to build the foundation for modern business applications in the company. In this role, you will apply general knowledge of modern infrastructure and cloud software engineering practices to build, secure and maintain the core capabilities used by the data and application teams to drive business value.
Key Accountabilities
Build the Generative AI platforms, systems and infrastructure and apply general knowledge of software development and infrastructure as code practices.
Design, develop, test, deploy, support and enhance moderately complex automated infrastructure and platform components.
Work closely with both data scientists, data engineers, product managers, GenAI Engineers in the company and internal customers to identify key areas of development for our generative AI platform.
Independently solve moderately complex issues with minimal supervision, while escalating more complex issues to appropriate staff.
Other duties as assigned
Qualifications
Minimum Qualifications
Bachelor’s degree in a related field or equivalent experience
Minimum of two years of related work experience
Experience building GenAI or ML platform systems for applications in the ML or Foundation Model model development lifecycle such as model training, data preparation, model evaluation, and model serving
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Equal Opportunity Employer, including Disability/Vet.