AI Engineer Lead

Federal Express Corporation•Coraopolis, PA

2d•Remote

About The Position

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.

Requirements

Prior experience translating complex operational, supply chain, freight routing, or transportation workflows into software models.
Demonstrate the ability to quickly master FedEx's physical footprint to optimize integrated Surface and Air execution.
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.
Extensive experience working with graph structures, ontologies, and data mapping tools designed to harmonize historically siloed or disparate data ecosystems.
Direct experience building automated CI/CD pipelines, containerized deployments (Docker/Kubernetes), and managing model lifecycles under strict corporate uptime and latency SLAs.
Strong capability to interface with research-focused Data Scientists and structural Domain Architects, acting as the engineering execution arm that makes their frameworks operational.
Master’s degree in Computer Science, Data Science, Engineering, or related field highly preferred.
5+ years of technical experience where they have a proven track record of architecting and building large-scale, novel AI systems

Responsibilities

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.
Write clean, efficient, and well-documented production-quality code to engineer scalable AI systems, knowledge graphs, and complex data pipelines.
Develop intelligent, LLM-driven AI Agents and advanced algorithms that possess a deep understanding of enterprise demand patterns and transportation networks.
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.
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.
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.