MLOps Engineering Manager

Our Client is one of the United States’ largest insurers, providing a wide range of insurance and financial services products with gross written premiums well over US$25 Billion (P&C). They proudly serve more than 10 million U.S. households with more than 19 million individual policies across all 50 states through the efforts of over 48,000 exclusive and independent agents and nearly 18,500 employees. Finally, our Client is part of one the largest Insurance Groups in the world. Job Summary The MLOps Engineering Manager leads the deployment, implementation, and optimization of machine learning pipelines to solve complex business challenges. The role involves both hands-on work and supervising a team to deliver effective machine learning engineering solutions for a line of business. The position applies in-depth knowledge of policies, procedures, and business objectives to make decisions and guide team. Performs work independently while receiving limited guidance. Key Responsabilities Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models. Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring. Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models. Creates strategic plans within span of control and implements them across one to two business domains. Collaborates with cross-functional teams to integrate machine learning solutions into production systems. Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows. Leads team to implement and manage tools and frameworks for efficient model training, testing, and deployment. Supervises, coaches, and guides direct reports. Leads team in supporting, maintaining, and modifying complex data products or solutions while managing data flow for enterprise applications. Requirements Minimum 5 years of experience in deploying and managing machine learning pipelines, or related. (MUST) Highly technical SME backgorund Experience in a leadership role within a fast-paced, technology driven environment Full English Fluency Skills / Capabilities Able to communicate complex technical concepts in a clear and concise manner. Ability to lead/manage others. Effectively coaches and delivers constructive feedback. Demonstrated analytical and demonstrated problem solving skills. Ability to drive multiple projects to successful completion. Full Stack Experience is desirable Software / Technical ML-OPS - Advance (Required) AWS - Advanced (Required) Python - Advanced (Required) Kubernetes/Cloud Development In-depth knowledge of machine learning frameworks and libraries. Familiar with DevOps practices and tools for continuous integration and deployment. Benefits This position comes with competitive compensation and benefits package: Competitive salary and performance-based bonuses Comprehensive benefits package Career development and training opportunities Flexible work arrangements (remote and/or office-based) Dynamic and inclusive work culture within a globally renowned group Private Health Insurance Pension Plan Paid Time Off Training & Development About Capgemini Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of over 340,000 team members in more than 50 countries. With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group €22.5 billion in revenues in 2023.

Location: Mexico City, Mexico, MX

Posted Date: 7/7/2025