AI/ML Engineer

Cardinal Health

Confirmed live today High trust

Quick summary

Work type
On-site
Location
—
Salary
$94,900–$122,400 / yr
Posted
3 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $199k
This role $109k
$76k most similar roles pay here $272k

This role pays less than 99% of similar roles. Most pay $162,000–$235,762 — the shaded band above. At the midpoint, this role pays about $109k versus about $199k for comparable roles.

Based on 240 similar postings.

Employer

About Cardinal Health

Cardinal Health is a global healthcare services and products company specializing in pharmaceutical distribution, medical products, and supply chain solutions for healthcare providers and pharmacies. Industry: Healthcare Distribution & Services

Cardinal Health currently has 38 open roles on FindRole.

Listed pay typically runs $94,900–$136,715 across 34 roles with salary data.

Most-posted roles

View all roles at Cardinal Health

At a glance

TL;DR · AI/ML Engineer

Role Posting Log in to save

AI/ML Engineer The AI/ML Engineer joins the Data & Analytics team to design and deliver enterprise-scale Generative AI solutions by partnering with product managers, software engineers, and business stakeholders. This role involves developing AI-powered applications including Large Language Models, Retrieval-Augmented Generation, AI Agents, and intelligent automation workflows. The engineer will transform complex business problems into production-grade systems while ensuring security and reliability across enterprise platforms. Key technical requirements include proficiency in Python, building REST APIs with FastAPI or Flask, and utilizing Docker and Kubernetes for containerization. Candidates must have experience with vector databases, LangChain, CrewAI, or Semantic Kernel, alongside familiarity with MLOps and LLMOps. The role requires expertise in cloud-native architectures on platforms like GCP, Azure, or AWS to build scalable solutions using both relational and NoSQL databases while implementing responsible AI and compliance controls.

Skills

Generative AI LLMs RAG Python LangChain LangGraph CrewAI FastAPI Flask Docker Kubernetes GCP Azure AWS CI/CD MLOps LLMOps Vector Databases REST APIs NoSQL

What you'll do

  • Design and deliver enterprise-scale Generative AI solutions including LLMs, RAG architectures, and AI Agents.
  • Develop production-grade machine learning systems and integrate them into existing enterprise platforms.
  • Build intelligent automation workflows and conversational experiences to solve complex business problems.
  • Translate business requirements from stakeholders into scalable, technical AI-powered products.
  • Implement MLOps and LLMOps practices for model deployment, monitoring, and observability.
  • Ensure all AI solutions comply with security, privacy, and responsible AI governance standards.
  • Develop and maintain REST APIs using frameworks like FastAPI or Flask to integrate AI services.
  • Manage the full lifecycle of AI products from initial prototyping to production deployment.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline (equivalent experience considered).
  • 4-8 years of experience in Software Engineering, Machine Learning Engineering, AI Engineering, or related fields (preferred).
  • Proficiency in Python and experience developing production-grade applications.
  • Experience building REST APIs using frameworks like FastAPI or Flask and working with relational and NoSQL databases.
  • Expertise in Generative AI solutions including LLMs, RAG architecture, vector databases, and AI Agent frameworks like LangChain or CrewAI.
  • Experience with containerization (Docker), Kubernetes platforms, and cloud providers such as GCP, Azure, or AWS.
  • Familiarity with MLOps/LLMOps, CI/CD pipelines, and implementing responsible AI security and compliance controls.
  • Strong communication skills to translate business requirements into technical solutions for both technical and non-technical audiences.

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