People Manager- ML Engineer

Our Technology Centers focus on creating a space where teams can come together to deliver business solutions that save and improve lives. An integral part of our companys IT operating model, Tech Centers are globally distributed locations where each IT division has employees to enable our digital transformation journey and drive business outcomes. These locations, in addition to the other sites, are essential to supporting our business and strategy. A focused group of leaders in each Tech Center helps to ensure we can manage and improve each location, from investing in growth, success, and well-being of our people, to making sure colleagues from each IT division feel a sense of belonging to managing critical emergencies. And together, we must leverage the strength of our team to collaborate globally to optimize connections and share best practices across the Tech Centers. Skills: People management, ML ops, ML engineer Key Responsibilities Leadership & People Management Lead, mentor, and grow a team of ML Engineers, MLOps Engineers, and Platform Engineers. Drive technical excellence, engineering best practices, and career development. Manage hiring, performance management, succession planning, and team engagement. Foster a culture of innovation, collaboration, accountability, and continuous improvement. MLOps & ML Platform Engineering Define and execute the technical strategy for enterprise MLOps and ML platform capabilities. Build and maintain scalable platforms for model training, deployment, monitoring, and lifecycle management. Standardize ML deployment processes and operational workflows. Establish best practices for model governance, reproducibility, and model monitoring. Implement automated retraining, validation, release, and rollback mechanisms. Cloud & Infrastructure Design and operate cloud-native ML platforms on AWS. Build scalable infrastructure using Kubernetes, containerization, and Infrastructure-as-Code. Partner with platform and security teams to ensure highly available and secure AI environments. Optimize platform performance, reliability, and cost. AI & Generative AI Enablement Enable enterprise adoption of Generative AI and LLM-based applications. Support AI engineering teams with production-ready RAG, Agentic AI, and model serving capabilities. Establish governance and monitoring frameworks for AI systems. Collaborate with architecture teams on AI platform roadmaps and standards. Operational Excellence Define SLAs, SLOs, monitoring, observability, and incident management processes. Drive automation across deployment, testing, monitoring, and infrastructure management. Ensure compliance, security, and responsible AI practices. Lead production support and root-cause analysis for platform issues