Enterprise AI/ML Engineer – Gen AI Platforms

Cardinal Health; Austin (TX); Sur place; USD 96 000 - 124 000; Plein temps

Avantages offerts par ce poste

Medical, dental and vision coverage

Paid time off

Health savings account (HSA)

401k savings plan

Access to wages before pay day (myFlex

Flexible spending accounts (FSAs)

Disability coverage

Work-Life resources

Paid parental leave

Healthy lifestyle programs

Résumé du poste

Cardinal Health seeks an AI/ML Engineer to design and deliver enterprise-scale Generative AI solutions. You will build production-grade ML systems and integrate AI capabilities into enterprise platforms, working with product managers and engineers throughout the lifecycle.

The role requires strong software engineering fundamentals and cloud-native development, plus experience with LLMs, RAG, AI Agents, and secure, observable deployments. Flexible collaboration across teams is expected.

Qualifications

• Bachelor’s degree or equivalent in a related field.
• Proven experience building production-grade AI/ML solutions.
• Strong Python and API development skills.
• Experience with modern AI/LLM tooling and cloud-native apps.

Responsabilités

• Design and deliver enterprise-scale Generative AI solutions.
• Build production-grade ML systems and integrate AI into enterprise platforms.
• Collaborate with product teams and software engineers across the lifecycle.

Connaissances

Python programming

REST APIs development

Agile SDLC

GitHub CI/CD

Docker

Kubernetes

Cloud platforms (GCP/AWS/Azure)

Excellent communication

Formation

Bachelor’s degree in CS/Engineering/Data Science/AI

Outils

FastAPI/Flask

GitHub Actions

GitLab

Azure DevOps

Jenkins/Harness

Docker

Kubernetes (GKE/AKS/EKS)

GCP/AWS/Azure

Description du poste

Cardinal Health seeks an AI/ML Engineer to design and deliver enterprise-scale Generative AI solutions. You will build production-grade ML systems and integrate AI capabilities into enterprise platforms, working with product managers and engineers throughout the lifecycle.

The role requires strong software engineering fundamentals and cloud-native development, plus experience with LLMs, RAG, AI Agents, and secure, observable deployments. Flexible collaboration across teams is expected.