[Remote] Manager, Engineering AI (GCP)

Note: The job is a remote job and is open to candidates in USA. Federal Express Corporation is seeking a Manager of Data and AI Engineering to lead a high-performing team in designing and deploying enterprise-grade data and AI solutions. This role involves bridging business priorities with technical execution, ensuring the delivery of reliable and scalable engineering roadmaps for data and AI systems while fostering collaboration across teams.ResponsibilitiesLead and mentor a high-performing team responsible for designing, developing, deploying, and operationalizing enterprise-grade data and AI solutionsBridge business priorities with technical execution, translating strategic objectives into scalable engineering roadmaps for data pipelines, MLOps frameworks, and production-ready AI systemsAccountable for the delivery of reliable, governed, secure, and maintainable solutions that enable intelligent automation, predictive insight, and advanced analytics across the organizationFoster engineering excellence, collaborate closely with data science, product, and business leaders, and grow technical talentManage, mentor, and develop high-performing technical teams, guiding Data Engineers, Machine Learning Engineers, and AI Engineers through complex challengesOwn the full talent lifecycle, including attracting, hiring, and onboarding top technical talent, managing performance, and fostering career developmentIdentify and solve problems proactively, driving initiatives forward and inspiring a culture of excellence and accountability within the teamThink strategically and operate effectively within ambiguous environments, translating complex business requirements into clear technical roadmaps and end-to-end architectural designsPossess a strong technical background and decision-making authority across the full AI stack, with hands-on proficiency in data engineering, ML system design, and MLOps practicesImplement enterprise standards for responsible AI, including model governance, fairness, explainability, and securityPrevent redundant or fragmented AI solutions by driving standardization and ensuring new systems integrate seamlessly with existing enterprise APIs and data ecosystemsUnderstand the risks associated with agent-based systems and design robust safeguards for automated systemsCommunicate effectively with stakeholders, articulating complex technical concepts, risks, and outcomes to both technical and non-technical audiencesCollaborate effectively across the organization, navigate complex stakeholder relationships, build consensus, and foster alignment in challenging situationsPromote disciplined engineering practices when transitioning solutions to production, ensuring all AI solutions are evaluated for scalability and maintainabilityEnsure AI solutions are designed to integrate with existing enterprise systems, APIs, and data ecosystemsManage technical projects using Agile/Scrum methodologiesSkillsProven experience managing, mentoring, and developing high-performing technical teams, with a strong ability to guide Data Engineers, Machine Learning Engineers, and AI Engineers through complex challengesDemonstrated ownership of the full talent lifecycle, including attracting, hiring, and onboarding top technical talent, as well as managing performance and fostering career developmentA self-starter mentality with a proactive approach to identifying and solving problems, driving initiatives forward, and inspiring a culture of excellence and accountability within the teamAbility to think strategically and operate effectively within ambiguous environments, translating complex business requirements into clear technical roadmaps and end-to-end architectural designsStrong technical background and decision-making authority across the full AI stack, with hands-on proficiency in: Data Engineering & Platforms: ETL/ELT, data warehousing, and big data technologies (e.g., Spark)Architecting scalable and maintainable machine learning systemsCI/CD, containerization (Docker, Kubernetes), automated model monitoring, feature stores, and lifecycle governanceDeep knowledge of modern data stacks and GCP services, particularly their AI/ML offeringsDeep conceptual and practical understanding of how generative AI systems work, with the ability to guide teams in designing efficient prompts and interactions to optimize model performance, accuracy, and costStrong command of AI cost dynamics (e.g., tokenization, request patterns) to implement effective cost-optimization strategiesExperience implementing enterprise standards for responsible AI, including model governance, fairness, explainability, and securityResponsible for preventing redundant or fragmented AI solutions by driving standardization and ensuring new systems integrate seamlessly with existing enterprise APIs and data ecosystemsUnderstanding of the risks associated with agent-based systems (e.g., cascading failures, uncontrolled API interactions) and the ability to design and enforce robust safeguards such as rate limiting, bounded execution, and controlled data accessExceptional communication and stakeholder management skills, with a proven ability to articulate complex technical concepts, risks, and outcomes to both technical and non-technical audiences, from individual contributors to senior leadershipA natural ability to collaborate effectively across the organization, navigate complex stakeholder relationships, build consensus, and foster alignment even in challenging situationsPromotes disciplined engineering practices over rapid experimentation when transitioning solutions to production, ensuring all AI solutions are evaluated for scalability, maintainability, and seamless integration within the broader enterprise ecosystemEnsures that AI solutions are designed to integrate with existing enterprise systems, APIs, and data ecosystems, avoiding the creation of isolated or siloed implementationsExcellent understanding of Agile/Scrum methodologies for managing technical projects, engineering backlogs, and delivering resultsBachelor's Degree in Information Systems, Computer Science, or a quantitative discipline such as Mathematics or Engineering and/or equivalent formal training or work experienceFive to eight (5-8) years equivalent work experience in measurement and analysis, quantitative business problem solving, simulation development and/or predictive analyticsExtensive knowledge in data engineering and machine learning frameworks including design, development and implementation of highly complex systems and data pipelinesExtensive knowledge in Information Systems including design, development and implementation of large batch or online transaction-based systemsStrong understanding of the transportation industry, competitors, and evolving technologiesExperience providing leadership in a general planning or consulting settingExperience as a leader or a senior member of multi-function project teamsStrong oral and written communication skillsCompany OverviewWe began our company by creating the world's next big thing from the ground up - and we've never slowed down, never stopped innovating, never stopped inventing. It was founded in undefined, and is headquartered in , with a workforce of 10001+ employees. Its website is https://careers.fedex.com/?utm_domicile=unspecified&utm_persona=unspecified&utm_trackedsource=srm_linkedin_company&utm_subco=FEC.