AI Engineer I – Production ML & Autonomous Agents

FedEx Corporation; Tennessee; Híbrido; USD 79.000 - 140.000; Tempo integral

Resumo da vaga

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.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

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

Formação

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

Ferramentas

Vertex AI

GKE

Cloud Run

BigQuery Vector Search

Cloud Dataflow

OR-Tools

Gurobi

PyTorch

TensorFlow

SQL

Descrição da vaga

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.