AI Engineer II — Production ML & MLOps

Federal Express Corporation; Memphis (TN); Presencial; USD 94.000 - 127.000; Tempo integral

Resumo da vaga

Federal Express Corporation is seeking an AI Engineer to design, develop, deploy, and maintain AI/ML solutions powering intelligent automation and advanced analytics across the enterprise.

You will write production‑quality code, build scalable AI systems including AI agents and data pipelines, and collaborate with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to move models from prototype to production.

Qualificações

• Bachelor's degree in Computer Science, Data Science, Engineering, or related field is required; Master’s is highly preferred.
• Must have independently built, trained, and iterated on multiple ML models with at least 2 years hands-on experience with a deep learning framework.
• Strong coding skills in Python, Java, or C, including API development and software design.

Responsabilidades

• Model Development & Implementation – Write clean, efficient, and well‑documented code to develop and implement machine learning and AI models for various business use cases. Prepare data inputs via preprocessing workflows.
• ML Pipelines & Operations (MLOps) – Design, develop, and maintain scalable ML pipelines for training, validation, inference, and deployment; monitor production models for drift and performance.
• Collaboration & Integration – Work with Data Scientists to transition prototypes into production; support integration of AI capabilities into enterprise workflows and platforms.
• Governance & Strategy – Ensure AI systems comply with governance, fairness, and security standards; evaluate new AI tech for business applicability.

Descrição da vaga

Federal Express Corporation is seeking an AI Engineer to design, develop, deploy, and maintain AI/ML solutions powering intelligent automation and advanced analytics across the enterprise.

You will write production‑quality code, build scalable AI systems including AI agents and data pipelines, and collaborate with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to move models from prototype to production.