AI/ML Engineer

What Data Science contributes to Cardinal Health The Data & Analytics Function oversees the analytics life-cycle in order to identify, analyze and present relevant insights that drive business decisions and anticipate opportunities to achieve a competitive advantage. This function manages analytic data platforms, the access, design and implementation of reporting/business intelligence solutions, and the application of advanced quantitative modeling. Data Science applies base, scientific methodologies from various disciplines, techniques and tools that extracts knowledge and insight from data to solve complex business problems on large data sets, integrating multiple systems.

We are seeking a product-oriented AI/ML Engineer who partners with product managers, software engineers, data engineers, and business stakeholders to design and deliver enterprise-scale Generative AI solutions. 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. The ideal candidate combines strong software engineering fundamentals with expertise in modern AI technologies and cloud-native application development. 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. This role is ideal for an engineer who enjoys working across the full lifecycle of AI products, from experimentation and prototyping to deployment, monitoring, optimization, and business adoption.

4-8 years of experience in Software Engineering, Machine Learning Engineering, AI Engineering, or related fields preferred. 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 (GCP), 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. What is expected of you and others at this level Applies comprehensive knowledge