Publix Super Markets, Inc., the largest employee-owned company in the U.S., is driven by a dynamic technology team of 2,300+ professionals delivering innovative solutions to 1,400+ stores and 260,000+ associates across 8 states. From IT security and platform engineering to architecture, software development, and infrastructure, we offer career opportunities at every level—internships through technical leadership. Join a company consistently ranked among Fortune’s “100 Best Companies to Work For” and help us build more than great subs—build the future of technology at Publix. We’re hiring a Lead Cloud Engineer for our Data & AI team. In this role, you’ll partner with cross-functional teams to design, develop, and scale AI solutions and frameworks. Your expertise will power innovative, generative AI technologies that solve complex challenges and deliver transformative business impact. Location: Hybrid (Lakeland, FL): To support our hybrid work model, candidates must reside, or be willing to relocate, within a commutable distance of Lakeland, FL. Work Model: Enjoy the best of both worlds—collaborate in person and innovate remotely. Interview Process: Candidates selected to advance in the interview process will be required to participate in at least one onsite interview at our Lakeland, FL campus. Why Join Us? • Hybrid Flexibility: Work remotely when you need focus time and join us onsite for high-impact collaboration and brainstorming sessions. • Operational Efficiency: Ensure technology solutions support efficient workflows and enable automation to improve operational effectiveness. • Cutting-Edge AI Projects: Drive innovation in AI platforms, integrating advanced tools and frameworks to solve complex business challenges. • Empowered Culture: We value autonomy, creativity, and continuous learning—your ideas shape the future of technology in our organization. Your Impact • Lead deployment and maintenance of AI infrastructure in Azure and GCP. • Responsible for maintaining Infrastructure as Code for AI and model workloads and compute resources. • Lead strategy meetings to improve shared AI infrastructure and platform services. • Own standards for deploying AI services, model workloads, and inference infrastructure in Azure and GCP. • Lead automation initiatives to improve CI/CD pipelines for AI application teams. • Lead troubleshooting efforts for AI infrastructure, model deployments, and pipeline failures. • Troubleshoot application and model workload deployment issues.