AI/ML Engineer

Summary

Cardinal Health is seeking a product-oriented AI/ML Engineer to design and deliver enterprise-scale Generative AI solutions. The role develops AI-powered applications using LLMs, RAG, AI agents, and intelligent automation workflows while managing the full lifecycle from prototyping and deployment through monitoring, optimization, and business adoption.

Responsibilities

• In this role, you will be hands-on in the development of AI-powered applications, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, intelligent automation workflows, and conversational experiences
• You will be responsible for transforming business problems into scalable AI solutions, building production-grade ML systems, integrating AI capabilities into enterprise platforms, and ensuring solutions are secure, reliable, and measurable
• Applies comprehensive knowledge and a thorough understanding of concepts, principles, and technical capabilities to perform varied tasks and projects
• May contribute to the development of policies and procedures
• Works on complex projects of large scope
• Develops technical solutions to a wide range of difficult problems
• Solutions are innovative and consistent with organization objectives
• Completes work; independently receives general guidance on new projects
• Work reviewed for purpose of meeting objectives
• May act as a mentor to less experienced colleagues

Skills

• * Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related discipline; equivalent experience considered
• * Strong proficiency in Python and experience developing production-grade applications
• * Experience building REST APIs using frameworks such as FastAPI, Flask, or similar technologies
• * Experience working within Agile and SDLC frameworks
• * Experience using GitHub and CI/CD platforms (GitHub Actions, GitLab, Azure DevOps, Jenkins, Harness, etc.)
• * Experience containerizing applications using Docker
• * Experience deploying and managing workloads on Kubernetes platforms (GKE, AKS, EKS, OpenShift, etc.)
• * Experience working with cloud platforms such as Google Cloud Platform (Google Cloud Platform), Azure, or AWS
• * Experience designing and implementing Generative AI solutions using Large Language Models (OpenAI, Gemini, Claude, Llama, Mistral, or similar)
• * Strong understanding of Retrieval-Augmented Generation (RAG) architecture and vector databases
• * Experience building AI Agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent
• * Experience integrating AI services through APIs and enterprise systems
• * Knowledge of prompt engineering, evaluation frameworks, and AI application optimization techniques
• * Familiarity with MLOps and LLMOps concepts, including model deployment, monitoring, observability, and governance
• * Experience implementing responsible AI, security, privacy, and compliance controls for enterprise AI solutions
• * Ability to develop scalable AI solutions using cloud-native architectures and event-driven patterns
• * Experience working with relational and NoSQL databases
• * Strong understanding of software design patterns, testing strategies, and production support practices
• * Proven ability to collaborate effectively with product teams to translate business requirements into AI-powered solutions
• * Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences
• * 4-8 years of experience in Software Engineering, Machine Learning Engineering, AI Engineering, or related fields preferred

Qualifications

Must Haves

• * Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related discipline; equivalent experience considered
• * Strong proficiency in Python and experience developing production-grade applications
• * Experience building REST APIs using frameworks such as FastAPI, Flask, or similar technologies
• * Experience working within Agile and SDLC frameworks
• * Experience using GitHub and CI/CD platforms (GitHub Actions, GitLab, Azure DevOps, Jenkins, Harness, etc.)
• * Experience containerizing applications using Docker
• * Experience deploying and managing workloads on Kubernetes platforms (GKE, AKS, EKS, OpenShift, etc.)
• * Experience working with cloud platforms such as Google Cloud Platform (Google Cloud Platform), Azure, or AWS
• * Experience designing and implementing Generative AI solutions using Large Language Models (OpenAI, Gemini, Claude, Llama, Mistral, or similar)
• * Strong understanding of Retrieval-Augmented Generation (RAG) architecture and vector databases
• * Experience building AI Agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent
• * Experience integrating AI services through APIs and enterprise systems
• * Knowledge of prompt engineering, evaluation frameworks, and AI application optimization techniques
• * Familiarity with MLOps and LLMOps concepts, including model deployment, monitoring, observability, and governance
• * Experience implementing responsible AI, security, privacy, and compliance controls for enterprise AI solutions
• * Ability to develop scalable AI solutions using cloud-native architectures and event-driven patterns
• * Experience working with relational and NoSQL databases
• * Strong understanding of software design patterns, testing strategies, and production support practices
• * Proven ability to collaborate effectively with product teams to translate business requirements into AI-powered solutions
• * Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences

Nice to Haves

• * 4-8 years of experience in Software Engineering, Machine Learning Engineering, AI Engineering, or related fields preferred

Benefits

• Medical, dental and vision coverage
• Paid time off plan
• Health savings account (HSA)
• 401k savings plan
• Access to wages before pay day with myFlexPay
• Flexible spending accounts (FSAs)
• Short- and long-term disability coverage
• Work-Life resources
• Paid parental leave
• Healthy lifestyle programs