Business SummaryThe 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.
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.
Pay RangePittsburgh, 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.
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. In New Jersey, any compensable Security and Walk time will be paid to non-exempt/hourly employees at the state minimum wage.
For details on our comprehensive benefits, click here. 
