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About This Role
What Application Development & Maintenance contributes to Cardinal Health
Information Technology oversees the effective development, delivery, and operation of computing and information services. This function anticipates, plans, and delivers Information Technology solutions and strategies that enable operations and drive business value.
Application Development & Maintenance performs configuration or coding to develop, enhance and sustain the organization's software systems in a cross-functional team environment through adherence to established design control processes and good engineering practices. This job family programs and configures end user applications, systems, databases and websites to achieve the organization's internal needs and externally-facing business needs. Application Development & Maintenance partners with business leaders, investigates user needs and conducts regular assessments, maintenance and enhancements of existing applications.
We are seeking a Workday AI Solutions Engineer to help HR identify and deliver high-value opportunities using Workday Extend and Workday’s AI offerings. Working across HR and technology teams, this individual will turn complex HCM needs into intuitive applications, automated workflows, integrations, and AI-enabled experiences. The role requires strong Workday technical expertise, practical knowledge of HCM processes, and the ability to evaluate opportunities based on business value, feasibility, security, and adoption readiness. This individual will help establish a sustainable foundation for expanding intelligent automation across HR.
Responsibilities
- Partners with HR leaders and functional teams to identify, evaluate, and prioritize opportunities to improve HCM processes through Workday Extend, Workday AI capabilities, agents, integrations, and workflow automation.
- Design, build, test, deploy, and support Workday Extend applications that simplify complex HR processes and provide intuitive experiences for employees, managers, and HR teams.
- Evaluate and implement Workday-delivered AI capabilities, Sana experiences, delivered agents, and custom agents based on business value, technical feasibility, security, and readiness for adoption.
- Develop proofs of concept and rapidly iterate with business stakeholders, moving validated solutions into secure, supportable, and scalable production implementations.
- Apply a strong understanding of Workday business processes, security, data models, reporting, APIs, and integrations to ensure Extend and AI solutions operate effectively within the broader Workday architecture.
- Design and implement integrations and orchestrations across Workday and third-party platforms using REST and SOAP APIs, Workday Orchestrate, Workday Studio, EIBs, and other appropriate integration technologies.
- Configure and manage permissions, domain security, business process security, authentication, and agent access in partnership with Workday Security, HR, Privacy, Legal, and enterprise technology teams.
- Establish technical standards and reusable design patterns for Workday Extend and AI solutions, including development, testing, deployment, monitoring, documentation, and production support.
- Monitor solution adoption, application performance, API consumption, agent activity, errors, and business outcomes to improve reliability, usability, and value.
- Stay current on Workday releases, platform capabilities, developer tools, AI offerings, and marketplace solutions. Assess their potential value and recommend practical adoption opportunities.
- Communicate solution options, tradeoffs, risks, and recommendations clearly to both technical and non-technical stakeholders.
- Promote responsible AI practices, including appropriate human oversight, data protection, transparency, security, testing, and ongoing monitoring.
Qualifications
- Demonstrated hands-on experience designing, developing, deploying, and supporting Workday Extend applications in a production environment preferred.
- Strong technical understanding of the Workday platform, including business process configuration, security, reporting, calculated fields, data sources, tenant configuration, and integration architecture preferred.
- Experience with Workday integration technologies such as REST and SOAP APIs, Workday Studio, EIBs, Cloud Connect, Workday Orchestrate, or event-driven integrations preferred.
- Proven ability to translate HCM business challenges into practical technical solutions, with experience supporting one or more areas such as Recruiting, Compensation, Talent, Payroll, Core HCM, Benefits, Time, or Absence preferred.
- Experience evaluating solution alternatives and clearly communicating technical feasibility, implementation complexity, risks, dependencies, and expected business value preferred.
- Strong problem-solving and troubleshooting skills, including the ability to diagnose issues across applications, business processes, security configurations, integrations, and data preferred.
- Experience implementing or supporting Workday AI capabilities, Sana for Workday, Workday-delivered agents, custom agents, Agent System of Record, Agent Gateway, or related agent-management capabilities preferred.
- Experience designing AI-enabled or agentic workflows that interact securely with Workday data, APIs, and business processes preferred.
- Familiarity with enterprise AI concepts such as large language models, prompt design, tool use, retrieval-augmented generation, human-in-the-loop controls, observability, and AI governance preferred.
- Experience connecting Workday with enterprise platforms such as Microsoft 365, ServiceNow, Salesforce, Google, or other HR technology solutions preferred.
- Experience developing reusable components, accelerators, templates, or technical frameworks for Workday Extend, integrations, or AI-enabled solutions preferred.
- Knowledge of data privacy, responsible AI, identity and access management, and security practices for systems containing sensitive workforce data preferred.
- Relevant Workday certifications, particularly in Workday Extend, integrations, platform, security, or HCM functional areas preferred.
What is expected of you and others at this level
- Applies advanced knowledge and understanding of concepts, principles, and technical capabilities to manage a wide variety of projects
- Participates in the development of policies and procedures to achieve specific goals
- Recommends new practices, processes, metrics, or models
- Works on or may lead complex projects of large scope
- Projects may have significant and long-term impact
- Provides solutions which may set precedent
- Independently determines method for completion of new projects
- Receives guidance on overall project objectives
- Acts as a mentor to less experienced colleagues
Anticipated salary range: $125,300 - $178,900
Bonus eligible: Yes
Benefits: Cardinal Health offers a wide variety of benefits and programs to support health and well-being.
- 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
Application window anticipated to close: 12/09/2026
- if interested in opportunity, please submit application as soon as possible.
The salary range listed is an estimate. Pay at Cardinal Health is determined by multiple factors including, but not limited to, a candidate’s geographical location, relevant education, experience and skills and an evaluation of internal pay equity.
""Candidates who are back-to-work, people with disabilities, without a college degree, and Veterans are encouraged to apply.
Cardinal Health supports an inclusive workplace that values diversity of thought, experience and background. We celebrate the power of our differences to create better solutions for our customers by ensuring employees can be their authentic selves each day. Cardinal Health is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, age, physical or mental disability, sex, sexual orientation, gender identity/expression, pregnancy, veteran status, marital status, creed, status with regard to public assistance, genetic status or any other status protected by federal, state or local law.
Salary Context
This $125K-$178K range is below the median for AI/ML Engineer roles in our dataset (median: $177K across 1606 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,253 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cardinal Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 13,052 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,382. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $125K to $178K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,382; Director: $272,300; VP: $235,830.
Cardinal Health AI Hiring
Cardinal Health has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $151K - $178K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (107) are outnumbered by mid-level (4,462) and senior (3,410) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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