This is a departmental posting to the Data & Technology org ONLY. All qualified candidates must be reporting up through one of the senior executives within the Data & Technology organization. If the candidate does not currently report to any leaders in the Data and Technology organization, they will not be considered.
The AI Engineer is responsible for designing, developing, deploying, and maintaining artificial intelligence and machine learning solutions that support intelligent automation, predictive insight, and advanced analytics across the enterprise. As a hands-on builder, this role applies software engineering principles to write production-quality code, build scalable AI systems, including AI Agents and data pipelines, and integrate AI models into new and existing business applications.
The AI Engineer collaborates closely with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to bring machine learning models from prototype to production. A critical part of this function is to ensure that AI use cases are transitioned from experimentation into reliable, governed, and business-ready solutions by owning their complete operational readiness. This includes implementing robust observability, defining Service Level Objectives (SLOs), and establishing clear incident response and rollback strategies for all AI services.
About the Role -
Enabling the "One FedEx" Ontology for Unified Network Design (N2.0)
To fully realize the Network 2.0 (N2.0) vision, FedEx requires a unified data and operational model that breaks down historical silos. This AI Principal Lead is the technical engine behind the Neptune Ontology, building the complex AI structures and knowledge graphs necessary for a true "One FedEx" Ontology. By engineering scalable AI and data pipelines that seamlessly integrate Surface and Air Design, this role enables leadership to model, simulate, and optimize the entire enterprise network as a single, cohesive ecosystem rather than disparate operating companies.
Optimizing the Tri-Color Strategy via Advanced Demand and Execution AI
Executing the Tri-Color network strategy requires unprecedented precision in forecasting and routing. This role brings advanced machine learning, predictive analytics, and LLM-driven AI Agents to the forefront of Network Planning. By developing intelligent algorithms that deeply understand Demand patterns, this lead ensures we can dynamically allocate volume to the right network layer (Purple, Orange, or White) at the right time. Furthermore, by integrating these AI models directly into operational applications, they drive automated, intelligent Execution outcomes—optimizing load planning, reducing empty miles, and maximizing yield across all transportation modes.
Ensuring Mission-Critical Reliability for N2.0 Operations
As AI transitions from experimental to operational, the systems driving N2.0 must be as reliable as our physical fleet. This Principal Lead does not just build models; they engineer the rigorous MLOps, CI/CD pipelines, and observability frameworks required to keep the Neptune Ecosystem running seamlessly. By establishing strict Service Level Objectives (SLOs) for latency, monitoring for data drift, and ensuring robust governance, this role guarantees that the AI powering our real-time Surface and Air execution is trusted, secure, and fully capable of operating at FedEx's global scale.
Essential Functions
- Hands-On Model & Graph Development: Write clean, efficient, and well-documented production-quality code to engineer scalable AI systems, knowledge graphs, and complex data pipelines.
- Domain-Driven AI Architecture: Develop intelligent, LLM-driven AI Agents and advanced algorithms that possess a deep understanding of enterprise demand patterns and transportation networks.
- MLOps & Enterprise Reliability: Design and maintain scalable ML pipelines for model training, validation, inference, and deployment. Partner with MLOps teams using established CI/CD practices to manage live deployment.
- Mission-Critical Governance: Own end-to-end operational readiness by establishing strict Service Level Objectives (SLOs) for system latency and availability. Implement robust observability frameworks to monitor for data drift, error rates, and automated rollback strategies.
- Cross-Functional Engineering: Collaborate closely with Data Scientists, Data Engineers, and Domain Architects to transition experimental models out of research phases into stable, secure, and business-ready production solutions.
Knowledge, Skills, and Abilities
- Essential Logistics Domain Acumen: Prior experience translating complex operational, supply chain, freight routing, or transportation workflows into software models. The candidate must demonstrate the ability to quickly master FedEx's physical footprint to optimize integrated Surface and Air execution.
- Production AI Engineering Depth: Proven track record as a hands-on software engineer specializing in scaling AI systems, RAG frameworks, and complex data pipelines into real-world production environments.
- Graph & Data Architecture: Extensive experience working with graph structures, ontologies, and data mapping tools designed to harmonize historically siloed or disparate data ecosystems.
- MLOps Infrastructure Fluency: Direct experience building automated CI/CD pipelines, containerized deployments (Docker/Kubernetes), and managing model lifecycles under strict corporate uptime and latency SLAs.
- Collaborative Leadership: Strong capability to interface with research-focused Data Scientists and structural Domain Architects, acting as the engineering execution arm that makes their frameworks operational.
Minimum Education:
Master’s degree in Computer Science, Data Science, Engineering, or related field highly preferred.
Minimum Experience:
5+ years of technical experience where they have a proven track record of architecting and building large-scale, novel AI systems
Domicile Information
The preferred locations for this position are Remote or Hybrid (Memphis TN or Pittsburgh PA.) However, we will consider candidates based elsewhere within the United States (excluding Alaska, Hawaii, and U.S. territories).
Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week.
Preferred Qualifications:
Pay Transparency:
Pay: Pay: Compensation Grade FEC E30
Additional Details: Application Criteria: Please submit your application, resume and complete the questionnaire by Monday, July 6th 2026.
Pay Transparency:
The compensation listed reflects the pay range or rate of pay reasonably expected for this posted position at the posted location or locations. If this opportunity includes multiple job levels, the pay information represents the ranges for each level in that job family. Actual pay is determined by several job-related factors permitted by law and relevant to the position, including, but not limited to, experience relative to the job, tenure, market level, pay at the location for this job, performance, schedule, and work assignment. In California, the compensation listed reflects the range or rate of pay reasonably expected for this posted position upon hire.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact [email protected].
Applicants have rights under Federal Employment Laws:
E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program:
