AI Engineer I

FedEx Corporation; Tennessee; Hybrid; USD 79,000 - 140,000; Full time

Job summary

FedEx Corporation in the United States is seeking an AI Engineer to design, deploy, and maintain AI/ML solutions across the enterprise, writing production-quality code and building scalable AI systems, including agents and data pipelines.

You will collaborate with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to bring models from prototype to production, ensure governance, observability, and operational readiness with SLOs and rollback strategies.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering or related field.
• Academic projects or internships applying data manipulation (Pandas) and basic deep learning (PyTorch/TensorFlow).
• Experience applying ML concepts to guided/real-world problems.

Responsibilities

• Deploy and operationalize ADK agents to Vertex AI, Cloud Run, and GKE.
• Build data preprocessing, feature engineering, and model training workflows.
• Design high-performance RAG architectures with Vertex AI Vector Search and BigQuery.
• Develop multi-agent systems and scalable tool-calling pipelines.
• Collaborate with Data Scientists and Platform teams; document model transparency.

Skills

Python programming

APIs & REST

Production-grade software

ML/DL concepts

LLMs & prompt engineering

AI agent frameworks

RAG architectures

SQL & data warehousing

Data pipelines

GCP (Vertex AI)

Kubernetes & Docker

Education

Bachelor’s degree in Computer Science, Data Science, Engineering or related field

Tools

Vertex AI

GKE

Cloud Run

BigQuery Vector Search

Cloud Dataflow

OR-Tools

Gurobi

PyTorch

TensorFlow

SQL

Job description

This position will be hybrid in: Memphis, TN / Plano, TX / Pittsburgh, PA. Candidates must live within a commutable distance from Memphis, Plano, or Pittsburgh to be eligible for this role. Employees will be required to work at a FedEx office location up to several times per week.

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 ensuring 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 AI services.

Job Responsibilities

Agent Deployment & LLMOps

• Deploy and operationalize ADK agents to Vertex AI Agent Engine (Reasoning Engines), Cloud Run, and Google Kubernetes Engine (GKE) using the Agent CLI and containerized Python runtimes.
• Establish automated evaluation suites using agents-cli eval, Vertex AI GenAI Evaluation Service, and continuous regression testing for reasoning, tool fidelity, and context relevance.
• Implement production-grade agent observability, session telemetry, step-by-step tracing, and latency monitoring using Cloud Trace, Cloud Logging, and Vertex AI Model Monitoring.

Model Development & Implementation

• Design, develop, and implement machine learning, optimization, and Operations Research solutions using clean, efficient, and maintainable production-quality code.
• Build and maintain data preprocessing, feature engineering, and model training workflows required for scalable model development.
• Optimize the performance, scalability, reliability, and maintainability of AI applications and deployed models.
• Evaluate, validate, and tune machine learning and optimization models to improve accuracy, efficiency, and business impact.

Retrieval & Semantic Data Pipelines

• Design and implement high-performance RAG (Retrieval-Augmented Generation) architectures integrated with ADK using Vertex AI Vector Search, BigQuery Vector Search, and Cloud Storage (GCS).
• Build scalable ingestion, chunking, and embedding pipelines using Python, Cloud Dataflow (Apache Beam), and Cloud Run Jobs.
• Integrate structured enterprise databases (BigQuery, Cloud SQL/AlloyDB pgvector, Firestore) as tool-accessible datastores for autonomous agents.

Agent Development & Orchestration (ADK & Python)

• Architect and develop multi-agent systems, workflow-based agents, autonomous tool-calling pipelines, and agent orchestration frameworks using the Google Agent Development Kit (ADK) in Python.
• Use the Google Agent CLI (agents-cli/adk) to scaffold, run, test, evaluate, and package agent projects across development, staging, and production environments.
• Implement advanced agentic patterns including graph-based workflow runtimes, hierarchical multi-agent delegation, human-in-the-loop (HITL) tool confirmations, and state/session memory management.
• Build custom agent tools, Model Context Protocol (MCP) integrations, and API connectors powered by foundation models on Vertex AI.

Collaboration & Integration

• Work closely with Data Scientists to transition research prototypes and experimental models into production-ready solutions.
• Partner with application and platform teams to integrate AI services, agents, and model endpoints into enterprise systems and workflows.
• Create and maintain documentation that supports model transparency, explainability, and business adoption.

Governance & Strategy

• Ensure AI solutions comply with enterprise governance, security, and Responsible AI standards.
• Evaluate emerging AI technologies, including generative AI and LLM-based solutions, to drive innovation and business value.

Preferred Skills/Knowledge/Experience

Core Technical & AI Proficiency

• Strong programming skills in Python and experience building APIs and production-grade software.
• Solid understanding of machine learning, deep learning, and model development using frameworks such as PyTorch, TensorFlow, or similar.
• Experience with LLMs, prompt engineering, fine-tuning, AI agent frameworks, and RAG architectures.
• Knowledge of Operations Research and optimization techniques, including mathematical programming, simulation, decision modeling, and network optimization.
• Experience with optimization solvers such as OR-Tools, Gurobi, or similar.
• Proficiency in SQL, data modeling, data warehousing, and data pipeline development.

Machine Learning & Data Engineering

• Experience developing, training, evaluating, and deploying machine learning models from prototype to production.
• Strong knowledge of feature engineering, model evaluation, and scalable data processing pipelines.
• Experience working with SQL and NoSQL databases to support AI and analytics workloads.

Software Engineering & MLOps

• Knowledge of software engineering best practices including Git, automated testing, CI/CD, and RESTful API development.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with MLOps practices including model deployment, monitoring, lifecycle management, performance tracking, and operational readiness.

Cloud & Platform Technologies

• Experience developing and deploying AI solutions on Google Cloud Platform (GCP), including Vertex AI, Agent Engine, Cloud Run, and GKE.
• Experience with cloud platforms such as GCP or Azure and associated AI/ML services.
• Familiarity with distributed data processing technologies such as Apache Spark or equivalent.

Collaboration & Communication

• Strong analytical, problem-solving, and communication skills with the ability to work effectively in Agile, cross-functional teams.
• Ability to communicate technical concepts to both technical and business stakeholders.
• Experience building user-facing applications using modern frontend frameworks (React, Vue.js, Angular, or similar) is a plus.

Minimum Education:

Bachelor’s degree in Computer Science, Data Science, Engineering or related field.

Minimum Experienc e:

Should have completed academic projects or internships involving data manipulation (Pandas), building simple models (Scikit-learn), and a basic understanding of a deep learning framework (PyTorch/TensorFlow). Experience is about applying classroom concepts to a specific, guided problem.

Pay Transparency: This compensation range is provided as a reasonable estimate of the current starting salary range for this role across all potential locations. If this opportunity includes multiple job levels, the range is a reasonable estimate of the current starting salary for the lowest level to the current starting salary of the highest level. Actual starting pay would be determined by experience relative to the job, market level, pay at the location for this job and other job-related factors An employee may be eligible for additional pay, premiums, or bonus potential. The Company offers eligible employees health, vision, and dental insurance, retirement plans, and tuition reimbursement

Pay: US Pay Range: $7,094.23 - $12,533.14/month

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 [e-mail].

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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 learn more about the E-Verify program:

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