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About This Role
Position: AI and Data Platform Engineer – Full Time/Exempt
Department: Information Technology
Reports to: Director, Cloud and System Engineering
Location: Arizona Cardinals (Tempe, AZ) NOTE: Starting 2028, this role will be based at the new Arizona Cardinals Training Facility in north Phoenix, AZ (https://www.azcardinals.com/news/new-arizona-cardinals-practice-facility-on-target-for-opening-in-2028).
Format: In-person
Note: Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of a student or employment Visas (including, but not limited to F-1, M-1, J-1, H-1B, OPT and/or CPT programs, etc.).
Cardinals Organizational Summary:
The Arizona Cardinals Football Club is a professional football team within the National Football League (NFL). We compete in the National Football Conference (NFC) West division and call State Farm Stadium, in Glendale, Arizona, our home.
As one of the oldest pro football franchises in the U.S., the Club has established itself as a sport and cultural hallmark within Arizona, as well as a trailblazer for diversity, equity, inclusion, and belonging (DEIB) initiatives and firsts around the League. Our vision is to build a leading sports organization that competes for championships, drives business results, creates loyal fans, develops proud employees, and inspires hope in our community. We are relentless in our pursuit of greatness on and off the field, while positively impacting our people, our fans, and our community.
If you want to join an organization that values putting the team first, doing what is right, growing with positivity, valuing every voice, while driving performance, we would love for you to join our team. Learn more about the Arizona Cardinals and see what’s happening here (https://www.azcardinals.com/community/).
Job Summary:
The Arizona Cardinals are seeking a highly motivated, innovative, and technically skilled AI and Data Platform Engineer to support the organization's expanding Artificial Intelligence, data management, and enterprise technology strategy.
This role serves as a critical bridge between modern data platforms, cloud-based AI services, business applications, and enterprise systems. The AI & Data Platform Engineer will be responsible for designing, administering, and optimizing cloud data and database platforms, supporting AI initiatives and use cases, enabling secure access to enterprise data, and integrating AI capabilities into business applications, workflows, and data solutions.
The ideal candidate possesses strong experience with modern cloud data platforms, database technologies, and AI solutions, with a passion for building secure, scalable, and data-driven systems that improve decision-making, operational efficiency, employee productivity, and business outcomes.
This position supports the Arizona Cardinals' business operations, football operations, stadium operations, and corporate functions while ensuring data integrity, security, governance, and compliance with organizational and league requirements. AI, cybersecurity, and data governance are strategic priorities of the Arizona Cardinals and the National Football League; therefore, this role will play a key part in enabling secure, responsible, and scalable AI adoption across the organization. Other responsibilities include event support, game day duties at games, training camp and extended trips. This position will be required to work at all home games and team functions at the State Farm Stadium located in Glendale, AZ.
Primary Job Duties:
The AI and Data Platform Engineer position will have the daily responsibilities, without limitation, to include the following:
AI Platform Administration & Enablement
- Administer and support enterprise AI platforms including Microsoft Copilot, Azure AI services, approved large language model (LLM) platforms, and future AI technologies.
- Partner with business units to evaluate, prioritize, and implement AI use cases that drive operational efficiency and business value.
- Support AI training, prompt development, adoption programs, and end-user enablement initiatives.
- Assist with AI model integrations, automation workflows, and AI-powered business processes.
- Monitor AI platform usage, performance, security, and adoption metrics.
- Serve on the Cardinals AI Council to manage the AI use case requests and communication with the NFL.
- Support AI governance initiatives, documentation, and compliance requirements.
- Collaborate with cybersecurity teams to ensure AI solutions are implemented securely and align with organizational policies.
Database & Data Systems Administration
- Design, administer, monitor, optimize, and maintain enterprise databases and data repositories.
- Ensure database performance, availability, reliability, backup, recovery, and disaster recovery capabilities.
- Manage SQL Server and other database technologies supporting business-critical applications.
- Support database migrations and modernization initiatives, including migration from traditional on-premises databases to PaaS and cloud-native database services.
- Develop and maintain database standards, procedures, documentation, and operational runbooks.
- Troubleshoot database and data platform issues while ensuring minimal disruption to business operations.
Data Integration & Enterprise Systems Support
- Design and support integrations between enterprise applications, cloud services, databases, and AI solutions.
- Develop and maintain APIs, data pipelines, ETL processes, and automation workflows.
- Enable secure access to structured and unstructured data for analytics, reporting, and AI use cases.
- Collaborate with application owners and business stakeholders to improve data quality and accessibility.
- Support data warehouse, reporting, business intelligence, and analytics platforms.
Data Governance & Security
- Maintain data integrity, quality, classification, retention, and lifecycle management practices.
- Support data governance initiatives and compliance with internal standards, league requirements, and regulatory obligations.
- Implement security controls for databases, AI platforms, and integrated systems.
- Assist with audits, reporting, and risk assessments related to data and AI environments.
- Ensure appropriate access controls and role-based permissions are maintained.
Collaboration & Innovation
- Partners with IT, Cybersecurity, Cloud Engineering, Football Operations, Business Intelligence, and business units to identify opportunities for innovation.
- Participate in technology evaluations, proof-of-concepts, and pilot programs involving AI and emerging technologies.
- Provide technical guidance and recommendations regarding enterprise data architecture, AI adoption, and systems modernization.
- Support stadium operations, special events, training camp, and game-day technology initiatives as required.
- Other duties as assigned.
Qualifications/Requirements
- Education: Bachelor's degree in Computer Science, Information Systems, Data Science, Information Technology, Software Engineering, or a related technical field
- Experience: At least 2 – 5 years of experience in in database administration, systems administration, cloud platforms, or related technology roles.
- AI & Automation
- + Knowledge of Artificial Intelligence, Generative AI, Machine Learning, and enterprise AI platforms. + Experience supporting AI tools such as Microsoft Copilot, Azure AI Services, OpenAI, Anthropic, or comparable solutions. + Understanding of prompt engineering, AI adoption practices, and AI governance principles. + Familiarity with automation platforms such as Microsoft Power Platform, Power Automate, Logic Apps, or similar technologies.
- Data Engineering & Modern Data Platforms
- + Support data lake and lakehouse architectures using technologies such as Azure Data Lake Storage, Amazon S3, Databricks, Microsoft Fabric, Delta Lake, and Apache Spark. + Experience with database performance tuning, optimization, monitoring, backup, recovery, and disaster recovery. + Partner with analytics and AI teams to create trusted, reusable data products and curated datasets for Power BI, AI applications, and advanced analytics. Experience supporting large-scale enterprise data environments. + Prepare and optimize enterprise datasets for Generative AI, machine learning, semantic search, and Retrieval-Augmented Generation (RAG) use cases. + Understanding of lakehouse architecture, medallion architecture, dimensional modeling, data warehousing, and data mesh/data product concepts. + Knowledge of data modeling, database architecture, and data integration methodologies. + Experience supporting large-scale enterprise data environments.
- Systems Integration
- + Experience developing and supporting APIs, integrations, ETL processes, and enterprise workflows. + Develop and maintain ETL/ELT pipelines using Azure Data Factory, Fabric, Databricks, AWS Glue, Lambda, Step Functions, or comparable services + Understanding of cloud-based integration services and modern data architectures. + Experience integrating AI services with enterprise applications and business systems.
- Cloud & Enterprise Technologies
- + Knowledge of Microsoft Azure cloud services and enterprise cloud architectures. + Familiarity with Microsoft 365, Power Platform, Active Directory, Entra ID, and enterprise collaboration tools. + Understanding of data warehousing, analytics, and business intelligence solutions.
- Cybersecurity & Governance
- + Understanding of cybersecurity best practices related to databases, AI platforms, and enterprise applications. + Knowledge of data governance, data privacy, retention, and compliance principles. + Experience implementing security and access controls across enterprise systems.
- Strong analytical and problem-solving abilities.
- Excellent organizational and project management skills.
- Ability to communicate complex technical concepts to technical and non-technical audiences.
- Strong customer service orientation and stakeholder management skills.
- Ability to manage multiple priorities in a fast-paced, highly visible environment.
- Commitment to continuous learning and staying current with emerging AI and data technologies.
- Experience supporting Generative AI deployments in an enterprise environment, preferred.
- Familiarity with data cataloging, data classification, and data governance platforms, preferred.
- Experience within professional sports, entertainment, media, or large event-based organizations, preferred.
- Experience supporting mission-critical operations requiring high availability and rapid issue resolution, preferred.
- Flexibility to work evenings, weekends, and holidays; hours will vary depending on business needs.
- Must complete all pre-employment forms and successfully pass a background check.
Cardinals Benefits/Perks Summary:
The Arizona Cardinals Football Club has developed a comprehensive benefits package that provides economic and inclusive benefits to full-time employees. The Cardinals benefits offer care, protection, and support for employees and their loved ones. The Cardinals organization encourages an environment that is conducive to living a healthy, productive, and flexible lifestyle.
Benefits and Perks include, but are not limited to:
- Health, Dental, and Vision Insurance options; effective the first day of hire
- 401 (k) retirement option with employer match contribution
- Paid Time Off Accruals (including sick time accruals)
- Paid Time Off for most Federal holidays
- Time off for Maternity, Paternity, Military, and Bereavement
- MDLIVE: 24/7 medical support
- Flexible Spending Accounts (FSA) & Health Care Saving Account options
- Discounts on Cardinals gear & paraphernalia
- Tuition reimbursement & Professional Growth opportunities
- Daily free lunch
- Complimentary season tickets
- Subsidized gym memberships
The Arizona Cardinals Football Club is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.
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Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 Arizona Cardinals, 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.
Arizona Cardinals AI Hiring
Arizona Cardinals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tempe, AZ, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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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