AI Engineer Lead

OP RaaS; Pittsburgh (Allegheny County); Hybrid; USD 180,000 - 240,000; Full time

Job summary

FedEx is seeking an AI Principal Lead to design and deploy scalable AI systems, including Neptune ontology and knowledge graphs, enabling unified network design across Surface and Air operations. You will own MLOps, CI/CD pipelines, observability, SLOs, and governance to ensure production-ready AI services at scale.

Collaborate with Data Scientists, Data Engineers, and Domain Architects to transition prototypes into reliable business-ready solutions.

Qualifications

• Master’s degree in CS, DS, or related field preferred.
• 5+ years architecting large-scale AI systems.
• Experience with production AI, ML Ops, and data pipelines.
• Proficiency with graph structures and ontologies.

Responsibilities

• Hands-On Model & Graph Development: write production-quality code to engineer scalable AI systems, graphs, and data pipelines.
• Domain-Driven AI Architecture: develop intelligent, LLM-driven AI Agents and algorithms for enterprise demand patterns and transportation networks.
• MLOps & Enterprise Reliability: design and maintain scalable ML pipelines, collaborate with MLOps teams on CI/CD and live deployment.
• Mission-Critical Governance: establish SLOs for latency; implement observability, data drift monitoring, and rollback strategies.
• Cross-Functional Engineering: collaborate with Data Scientists, Data Engineers, and Domain Architects to productionize models.

Skills

Hands-on AI engineering

Knowledge graphs

MLOps & CI/CD

Data pipelines

LLM-driven AI Agents

Docker & Kubernetes

Enterprise reliability

Domain logistics knowledge

Education

Master’s degree in CS/DS/Engineering

Tools

Docker

Kubernetes

CI/CD tooling

Job description

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.