[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.
Responsibilities
- Lead and mentor a high-performing team responsible for designing, developing, deploying, and operationalizing enterprise-grade data and AI solutions
- Bridge business priorities with technical execution, translating strategic objectives into scalable engineering roadmaps for data pipelines, MLOps frameworks, and production-ready AI systems
- Accountable for the delivery of reliable, governed, secure, and maintainable solutions that enable intelligent automation, predictive insight, and advanced analytics across the organization
- Foster engineering excellence, collaborate closely with data science, product, and business leaders, and grow technical talent
- Manage, mentor, and develop high-performing technical teams, guiding Data Engineers, Machine Learning Engineers, and AI Engineers through complex challenges
- Own the full talent lifecycle, including attracting, hiring, and onboarding top technical talent, managing performance, and fostering career development
- Identify and solve problems proactively, driving initiatives forward and inspiring a culture of excellence and accountability within the team
- Think strategically and operate effectively within ambiguous environments, translating complex business requirements into clear technical roadmaps and end-to-end architectural designs
- Possess 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 practices
- Implement enterprise standards for responsible AI, including model governance, fairness, explainability, and security
- Prevent redundant or fragmented AI solutions by driving standardization and ensuring new systems integrate seamlessly with existing enterprise APIs and data ecosystems
- Understand the risks associated with agent-based systems and design robust safeguards for automated systems
- Communicate effectively with stakeholders, articulating complex technical concepts, risks, and outcomes to both technical and non-technical audiences
- Collaborate effectively across the organization, navigate complex stakeholder relationships, build consensus, and foster alignment in challenging situations
- Promote disciplined engineering practices when transitioning solutions to production, ensuring all AI solutions are evaluated for scalability and maintainability
- Ensure AI solutions are designed to integrate with existing enterprise systems, APIs, and data ecosystems
- Manage technical projects using Agile/Scrum methodologies
Skills
- Proven 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 challenges
- Demonstrated ownership of the full talent lifecycle, including attracting, hiring, and onboarding top technical talent, as well as managing performance and fostering career development
- A 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 team
- Ability to think strategically and operate effectively within ambiguous environments, translating complex business requirements into clear technical roadmaps and end-to-end architectural designs
- Strong 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 systems
- CI/CD, containerization (Docker, Kubernetes), automated model monitoring, feature stores, and lifecycle governance
- Deep knowledge of modern data stacks and GCP services, particularly their AI/ML offerings
- Deep 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 cost
- Strong command of AI cost dynamics (e.g., tokenization, request patterns) to implement effective cost-optimization strategies
- Experience implementing enterprise standards for responsible AI, including model governance, fairness, explainability, and security
- Responsible for preventing redundant or fragmented AI solutions by driving standardization and ensuring new systems integrate seamlessly with existing enterprise APIs and data ecosystems
- Understanding 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 access
- Exceptional 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 leadership
- A natural ability to collaborate effectively across the organization, navigate complex stakeholder relationships, build consensus, and foster alignment even in challenging situations
- Promotes 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 ecosystem
- Ensures that AI solutions are designed to integrate with existing enterprise systems, APIs, and data ecosystems, avoiding the creation of isolated or siloed implementations
- Excellent understanding of Agile/Scrum methodologies for managing technical projects, engineering backlogs, and delivering results
- Bachelor's Degree in Information Systems, Computer Science, or a quantitative discipline such as Mathematics or Engineering and/or equivalent formal training or work experience
- Five to eight (5-8) years equivalent work experience in measurement and analysis, quantitative business problem solving, simulation development and/or predictive analytics
- Extensive knowledge in data engineering and machine learning frameworks including design, development and implementation of highly complex systems and data pipelines
- Extensive knowledge in Information Systems including design, development and implementation of large batch or online transaction-based systems
- Strong understanding of the transportation industry, competitors, and evolving technologies
- Experience providing leadership in a general planning or consulting setting
- Experience as a leader or a senior member of multi-function project teams
- Strong oral and written communication skills
Company Overview