Manager, Engineering AI (GCP)

Federal Express Corporation•Plano, TX

3d•$9,208 - $20,872•Hybrid

About The Position

The Manager of Data and AI Engineering leads and mentors a high-performing team responsible for designing, developing, deploying, and operationalizing enterprise-grade data and artificial intelligence solutions. This role bridges business priorities with technical execution, translating strategic objectives into scalable engineering roadmaps for data pipelines, MLOps frameworks, and production-ready AI systems. The Manager is accountable for the delivery of reliable, governed, secure and maintainable solutions that enable intelligent automation, predictive insight, and advanced analytics across the organization. By fostering engineering excellence, collaborating closely with data science, product, and business leaders, and growing technical talent, this position plays a critical role in scaling the organization's ability to leverage data and AI effectively while ensuring alignment with enterprise architecture and responsible AI standards.

Requirements

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: ETL/ELT, data warehousing, and big data technologies (e.g., Spark).
Architecting scalable and maintainable machine learning systems.
MLOps Practices: 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.

Nice To Haves

A related advanced degree may offset the related experience requirements.

Responsibilities

Leads and mentors a high-performing team responsible for designing, developing, deploying, and operationalizing enterprise-grade data and artificial intelligence solutions.
Translates 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.
Fosters engineering excellence, collaborates closely with data science, product, and business leaders, and grows technical talent.
Takes ownership and responsibility for the support the design, build, test and maintain data pipelines at big data scale.
Leads a team responsible for modeling and development to support operations initiatives, strategic programs and new products and solutions.
Uses expertise to lead a team that initiates and delivers projects and develops end-to-end solutions that drive business results, including proofs-of-value for new business problems and production-ready solutions for operations and customer-facing products.
Understands and ensures development of solutions supporting the movement of data and information assets following API-First / Service-Oriented Architecture principles.
Advances the business' broad capabilities to use and deploy cutting edge data science and machine learning tools in Dataworks projects, platforms and products.
Provides expert consultation and thought leadership to senior management.
Mentors other team members to drive results and effectively collaborates with cross-functional teams to deliver business goals.