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About This Role
CBIZ, Inc. (NYSE: CBZ) is a leading professional services advisor to middle-market businesses nationwide. With industry knowledge and expertise in accounting, tax, advisory, benefits, insurance, and technology, CBIZ delivers actionable insights to help clients anticipate what is next and discover new ways to accelerate growth. CBIZ has more than 9,500 team members across 23 major markets coast to coast.
CBIZ strives to be our team members' employer of choice by creating an environment where team members are appreciated, recognized for their contributions, and provided with opportunities to grow, both personally and professionally, throughout their careers.
Together, CBIZ and CBIZ CPAs are ranked as one of the top providers of accounting services in the United States. CBIZ CPAs is an independent CPA firm that provides audit, review and attest services, while CBIZ provides business consulting, tax and financial services. In certain jurisdictions, CBIZ CPAs operates under its previous name, Mayer Hoffman McCann P.C.
Minimum Qualifications
- Bachelor’s degree or equivalent experience required
- At least 6 years of related experience required
- Minimum 3 years of supervisory experience required
- A degree in Information Technology, Computer Systems Analysis, or Management Information Systems is preferred
- PMP preferred
- Demonstrated knowledge of healthcare industry regulations, policies, and procedures
- Demonstrated knowledge and experience working with business stakeholders and leaders, IT project planning, delivery, and execution using Traditional and Agile SDLC methodologies
- Experience with VSTS, Jira, and/or other team/project collaboration tools
- Ability to work in and lead a team, while communicating and coordinating with peer teams
- Ability to formulate, document and recommend new policies and procedures
- Demonstrated ability to communicate verbally and in writing throughout all levels of an organization, both internally and externally
- Ability to travel as required by business and on-call availability
- Ability to lift up to 50lbs
About the Role
We are seeking an Artificial Intelligence (AI) Business Solutions (Manager) to join our AI team at CBIZ. As a Manager, you will play a critical role in creating impactful AI applications across the organization, working as a critical liaison between Product Engineering, Business Transformation teams, and business stakeholders to ensure technology solutions deliver measurable business value aligned with strategic priorities.
Essential Functions and Primary Duties
- Serve as the primary liaison between Product Engineering teams, Business Transformation teams, and business stakeholders.
- Build trusted relationships with business leaders, operational teams, and end users to understand evolving business needs, priorities, and challenges.
- Communicate product roadmaps, enhancement progress, release updates, and upcoming capabilities to stakeholders.
- Manage the intake process for enhancement requests, bug reports, and emerging business use cases while ensuring proper documentation and business justification, determining alignment with existing platform capabilities and strategic objectives.
- Facilitate prioritization discussions between stakeholders and product teams to align product investments with business objectives.
- Set appropriate expectations regarding product capabilities, timelines, design decisions, and roadmap priorities.
- Monitor user feedback, adoption trends, and platform usage patterns to identify opportunities for increased business value and user engagement.
- Coordinate User Acceptance Testing (UAT) activities for product releases, enhancements, and new capabilities.
- Develop testing strategies and collaborate with business stakeholders to ensure appropriate validation prior to production deployment.
- Evaluate release scope and determine the level of stakeholder participation required based on business impact and change magnitude.
- Facilitate testing sessions, collect feedback, document findings, and partner with engineering teams on prioritization of identified issues.
- Conduct user experience reviews and provide objective feedback regarding usability, intuitiveness, workflow efficiency, and overall user experience.
- Continuously update your knowledge of the latest in AI (e.g., model performance, context engineering, industry trends, application limitations) to feed the product pipeline and ensure enablement programs are best-in-class.
- Foster a collaborative and inclusive culture, modeling consultative, customer-focused leadership internally and externally.
Lead analysts and senior analysts to deliver high quality products, insights, and supporting materials and execute on defined objectives. *
Preferred Qualifications
- Bachelor’s degree in Business, Accounting, Taxation, Computer Science, Data Science, Information Systems, Education, or a related field.
- 4-7 years of experience in solutions engineering or consulting, enablement, training, or AI/enterprise software roles, ideally with a focus on prompt/context engineering, AI, and/or agents.
- Hands-on experience with LLMs (e.g., ChatGPT, Gemini, etc.), including creating, refining, and evaluating prompts/context, and agentic orchestration (e.g., MCP).
- Demonstrated ability to identify, validate, and prototype or build high value AI prompts or agents, working with key stakeholders to understand business needs.
- Experience in designing and delivering engaging training sessions, workshops, or enablement materials on technical topics for diverse audiences.
- Strong technical and business acumen; able to bridge the gap between end-user needs and AI technology solutions.
- Exceptional communication, facilitation, and storytelling skills; able to engage and influence both technical and non-technical stakeholders.
- Effective project management skills, able to juggle multiple initiatives and deliver results in a fast-paced environment.
- Creative, tenacious, analytical, and solution-oriented approach to problem solving.
- Customer-first mindset with experience driving technology adoption and change.
- Experience in the accounting, tax, benefits, or professional services industry is a plus.
- Willingness to travel up to 10-15% for training, client meetings, conferences, and events.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CBIZ, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. 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: $229,300; Director: $272,300; VP: $235,830.
CBIZ AI Hiring
CBIZ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $263,911, 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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