Description
Domicile Information
This is a hybrid position located in Memphis, TN or Pittsburgh, PA. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week.
Business Summary
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.
Essential Functions
About the Role - The AI Engineer Lead is a strategic, technically deep, hands-on engineering role responsible for designing, developing, deploying, and maintaining production-grade artificial intelligence and machine learning solutions across the enterprise. Moving far beyond experimental sandboxes, this Lead acts as the primary technical engine behind the Neptune Ontology, constructing the complex AI structures, Retrieval-Augmented Generation (RAG) systems, and knowledge graphs necessary to power the Network 2.0 (first and last mile (N2.0)) and Network 3.0 (middle mile, (N3.0)) visions.
While highly technical, success in this role is fundamentally tied to an understanding of physical logistical network layers. This Lead translates real-world transportation constraints into data structures, bridging the gap between data science experimentation and massive operational readiness to build a unified, cohesive "One FedEx" ecosystem.
- Tri-Color Network Optimization: Build production systems that dynamically allocate volume to the appropriate network layer-Purple (Air), Orange (Surface), or White (Third-Party)-to directly optimize load planning, reduce empty miles, and maximize operational yield.
- 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 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.
Pay Range
Pittsburgh, PA - $11,766.26/mo to $15,884.45/monthly
Memphis, TN - $11,177.95/mo to $15,090.23/monthly
Preferred Qualifications:
Pay Transparency:
Pay:
Additional Details: Application Criteria: Please submit your application, resume and complete the questionnaire by Monday, August 17, 2026.
For details on our comprehensive benefits, click .
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 [protected info].
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: