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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 forward\-thinking Workday AI Solutions Integration Consultant to lead the implementation, configuration, and optimization of Workday’s next\-generation AI capabilities—specifically Workday Everywhere, Workday Assistant, and Workday Illuminate. This role combines AI innovation with Workday integration expertise to deliver smart, scalable, and secure workflows that enhance business operations. The specialist will drive user experience transformation, streamline HR workflows, and support enterprise\-wide adoption of intelligent automation.
Responsibilities
- Lead deployment of AI agents such as Recruiter Agent and Performance Review Agent.
- Design, build, and deploy custom AI agents using Workday’s low\-code/no\-code tools and the Workday Build platform.
- Configure anomaly detection, auto\-filling, and contextual automation features across HR modules.
- Create AI\-powered applications and widgets using Workday Extend to embed personalized AI assistance directly into user workflows.
- Enable and configure Workday Assistant across web and Microsoft Teams environments.
- Develop guided workflows for time\-off requests, job changes, and employee inquiries.
- Monitor usage analytics and optimize conversational flows for accuracy and efficiency.
- Oversee integration of Workday Assistant into Microsoft Teams.
- Ensure seamless access to approvals, dashboards, and notifications within the flow of work.
- Coordinate with IT and HR administrators to manage app permissions and security protocols.
- Leverage Expanded AI Gateway APIs to embed Workday’s AI services into applications, including document intelligence for classification and data extraction.
- Automate and connect processes across Workday and third\-party systems using AI\-enabled Workday Orchestrate.
- Partner with HR, Finance, and other stakeholders to analyze requirements and translate business needs into technical designs for Illuminate solutions.
- Monitor Workday releases, especially Illuminate features and developer tools, to assess impact and recommend new functionality.
- Develop integrations and automations that uphold high standards for data integrity, security, and responsible AI practices.
- Provide technical support and root cause analysis for AI\-driven processes, integrations, and extensions.
- Use Developer Copilot to automate the generation of code and process documentation.
Qualifications
- Strong foundation of Workday experience with Extend apps, REST APIs and event\-based integrations preferred
- Workday functional: Understanding of HCM, Security, Business Process Framework (BPF), Reporting, configuration, tenant setup, and Workday governance preferred.
- Proven experience embedding AI agents onto enterprise platforms like Salesforce, ServiceNow, Microsoft, where agents take actions in workflows preferred.
- AI/Automation \& Agent Model Skills: Understanding agent lifecycle, skill/tool models, workflow\-to\-agent mapping, data\-driven automations, triggers, and execution modes. Ability to manage lifecycle actions such as Discover, Register, Configure, and Activate agents preferred.
- Comfort with tool\-using/multi\-agent patterns: agents calling APIs, following policies, and operating under enterprise security and audit preferred
- API \& Integration Skills: Experience with REST/SOAP APIs, authentication patterns, API gateway concepts, rate limits, telemetry, and integrating via Workday Public APIs, Graph API, RaaS, and ASOR endpoints preferred.
- Architecture \& Design: Knowledge of enterprise architecture, system boundaries, secure/scalable agent design, and multi\-system agent interactions preferred.
- Vendor/3rd\-Party Integration: Coordinating with external partners on APIs, skills/tools, authentication, compliance, and agent registration 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: $123,400 \- $176,300
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: 09/15/2026 \*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 $123K-$176K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($149K) sits 31% below the category median. Disclosed range: $123K to $176K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cardinal Health AI Hiring
Cardinal Health has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US. Compensation range: $103K - $176K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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