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About This Role
Salary Range
$104,361.28-$135,669.26 DOE
Grade
124
Work Schedule
Summer: Mon-Thurs; 7:00 am- 6:00 pm
Non Summer: Mon-Fri; 8:00 am- 5:00 pm
Work Calendar
12 months
Maricopa Summary
10 Colleges. Unlimited Opportunities.
The Maricopa County Community College District is one of the largest community college systems in the nation. Home to 10 individually accredited community colleges and 31 satellite locations, we proudly serve students in every corner of the Valley. Each day, our dedicated faculty, staff, and administrators, live out our vision—creating excellence in education for a better world.
We focus on people—not profits.
With 100% acceptance, zero rejections, and affordable tuition, we provide the flexibility and support our students need to succeed in and beyond the classroom. Discover how we’re changing college.
We don’t just support our community—we help build it.
We are the largest provider of workforce development training in the state. The activities of our colleges and their students support one out of every 28 jobs in Maricopa County. Learn about our economic impact.
We believe our employees are our most valuable asset.
Our 10 colleges and District Office support nearly 10,000 jobs and careers throughout Greater Phoenix.
Join us in making a real difference in the lives of over 140,000 college students each year.
Benefits
Maricopa County Community College District (MCCCD) is committed to providing a competitive and comprehensive benefits program that supports our employees' and their families' health and well-being. Therefore, the MCCCD benefits support every stage of life and are designed to meet the diverse needs of our community. Explore the wide range of benefits and perks available to eligible employees at MCCCD:
Affordable and Comprehensive Benefits Package:
- Nationwide Medical, Dental, and Vision Coverage
- Paid Time Off: Vacation, Sick Leave, and Personal Time
- 20 Paid Observed Holidays
- Company-paid Life Insurance, AD&D, and Short-Term Disability plans, with the option to purchase supplemental coverage
- Arizona State Retirement System (ASRS) Pension, including Long Term Disability and Retiree Health Insurance with 100% employer-matching contributions
- Optional Retirement Plans: 403(b), 457(b), Roth 403(b), Roth 457(b)
- Tuition Reimbursement for employees and dependents
- Annual Professional Development Funding
- Flexible Work Schedules
Employee Health & Wellness Programs:
- District-Wide Wellness Program with Workshops and Webinars
- Monthly Health & Wellness Calendar and Newsletter
- Virta Diabetes Reversal Program and Support Groups
- Employee Assistance Program (EAP)
- Sight-On-Site Eye Care Services
- Pre-Retirement Planning Events
- Qualifying Employer for Public Service Loan Forgiveness (potential loan forgiveness for federal Direct Loans after meeting repayment requirements and working full-time for an eligible employer)
Job Summary
Information Technology Services (ITS) at the MCCCD is looking for a highly skilled and hands-on Manager of AI/ML Engineering, to serve as both a technical leader and people manager for the District’s AI/ML engineering function. This role is responsible for designing, deploying, scaling, and supporting production-ready AI and machine learning solutions that advance student success, operational efficiency, workforce readiness, institutional effectiveness, and responsible AI adoption across the District.
This position combines technical depth, engineering leadership, and team management. The Manager of AI/ML Engineering, will lead a team of AI/ML engineers, developers, analysts, and related technical staff while remaining actively involved in solution architecture, model deployment, AI application integration, workflow orchestration, and operational support. The role will work closely with data engineering, architecture, governance, cybersecurity, analytics, and business stakeholders across academic, student, administrative, and operational domains.
The ideal candidate is a strong technical builder and practical leader who can establish engineering standards, mentor team members, and deliver scalable, secure, and reliable AI solutions in a complex enterprise environment.
Essential Functions
20% - AI/ML Engineering and Solution Delivery
Design, build, test, deploy, and maintain enterprise AI and machine learning solutions that support reporting, automation, decision support, intelligent search, workflow optimization, and user productivity.
Operationalize predictive models, natural language processing solutions, retrieval-based systems, recommendation workflows, classification services, and generative AI-enabled applications.
Build reusable AI/ML engineering patterns, services, and components to improve scalability, consistency, and speed of delivery.
Integrate AI/ML capabilities with enterprise data platforms, business applications, APIs, and workflow systems.
Support both real-time and batch-based inference and processing patterns as appropriate.
20% - Cloud and Platform Engineering
Build and maintain AI/ML solutions using AWS as the primary cloud platform.
Develop and support workflows using AWS services such as SageMaker, S3, Lambda, Bedrock, IAM, CloudWatch, Step Functions, API Gateway, ECS/EKS, and related technologies as appropriate.
Apply best practices for performance optimization, monitoring, logging, access control, resilience, model lifecycle management, and cost awareness in AWS-based environments.
Support modernization of manual, rule-based, or legacy workflows into scalable AI-enabled services.
20% - Governance, Security, and Responsible AI
Ensure AI/ML solutions comply with District governance, privacy, security, and access-control requirements.
Support responsible AI practices by incorporating guardrails, human review, prompt controls, output monitoring, and safe handling of sensitive data.
Work closely with governance, legal, compliance, and cybersecurity stakeholders to align AI implementations with District policy and institutional expectations.
Contribute to trustworthy and well-governed AI adoption that supports institutional decision-making and operational efficiency.
20% - Team Leadership and People Management
Supervise, mentor, and develop AI/ML engineers, AI developers, or related technical staff.
Assign work, establish priorities, provide technical coaching, and support employee development.
Conduct code reviews, design reviews, model/solution reviews, and technical guidance to ensure quality and consistency across the team.
Build a high-performing engineering culture focused on accountability, experimentation, responsible AI use, documentation, collaboration, and continuous improvement.
Help identify staffing, skill development, tooling, and process needs for a growing AI engineering function.
10% - Production Reliability and Operational Excellence
Establish and enforce standards for development, testing, deployment, release management, monitoring, documentation, and supportability of AI/ML systems.
Improve engineering practices related to CI/CD, MLOps, LLMOps, versioning, observability, model evaluation, prompt/version control, and issue resolution.
Ensure AI solutions include appropriate controls for output quality, traceability, auditability, human oversight, and operational support.
Lead troubleshooting of model failures, workflow breakdowns, hallucination or output quality issues, prompt regressions, latency bottlenecks, and production incidents, and drive root-cause resolution.
10% - Cross-Functional Collaboration
Partner with data architects, data engineers, governance leaders, analysts, institutional research teams, application teams, and business stakeholders to translate requirements into scalable AI solutions.
Work with technical and functional teams to identify and prioritize high-value use cases and implementation opportunities.
Participate in roadmap planning, solution estimation, prioritization, vendor/tool evaluation, and technical design discussions.
Communicate technical tradeoffs, risks, and implementation approaches clearly to both technical and non-technical audiences.
Performs related duties as assigned
Minimum Qualifications
Bachelor’s Degree from a regionally accredited institution in computer science, data science, software engineering, information systems, artificial intelligence, mathematics, statistics, or a related field, and six (6) years of progressively responsible experience in AI engineering, machine learning engineering, software engineering, and data engineering. Experience must include three (3) years of deploying AI/ML solutions in cloud-based environments, and two (2) years of leading projects, mentoring technical staff or teams supporting enterprise AI solutions in a production environment.
OR
An equivalent combination of the conferred degree, education, certification and directly related full-time work experience as described above sufficient to successfully perform the essential duties of the job such as those listed above, unless otherwise subject to any other requirements set forth in law or regulation, may be considered.
AND
AWS Certified Machine Learning; AWS Certified Solutions Architect; AI/ML frameworks, data science, or advanced analytics certifications
Desired Qualifications
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Systems, Engineering, or a related discipline.
- Experience performing a similar role in higher education, public sector, healthcare, or other complex multi-stakeholder environments.
- Experience with generative AI, large language model applications, retrieval-augmented generation, vector search, intelligent automation, or decision-support workflows.
- Experience collaborating with data engineering, governance, architecture, analytics, and cybersecurity teams.
- Experience implementing responsible AI controls and model/service monitoring.
- Experience modernizing manual or legacy processes into AI-enabled services or intelligent workflows.
Special Working Conditions
There is a minimum in-office requirement.
The selected candidate will need to reside in the Phoenix area or relocate within an agreed upon timeframe.
This role may involve after hours work and non-regular business hours.
May be required to sit for a prolonged period of time; viewing a computer monitor.
May be required to present in large meetings and sponsored events
ITS does not sponsor individuals for any type of work visas
How to Apply
Applicants are strongly encouraged to submit a cover letter that details how the applicant meets the minimum and desired qualifications. A cover letter may further highlight an applicant's knowledge, skill and abilities. All minimum requirements must be met at the time of the application.
Additional materials will not be accepted after the job posting has closed.
Missing materials or incomplete employment history will not be considered.
Please ensure your materials clearly provide the following information.
- Clearly illustrate how prior experience, knowledge and education meet the minimum and desired qualifications for this position.
- Indicate whether former or current employment is Full-Time or;
- Part-Time employment (must include number of hours worked)
- Provide employment history in a month/year format (e.g., 09/07 to 10/11) including job title, job duties, for each position held and name of employer for each position.
- Three professional references, preferably current and/or former supervisors. If references are not provided in resume upon application, they will be requested at time of interview.
Maricopa County Community College District (MCCCD) complies with the Americans with Disabilities Act (ADA) of 1990. Qualified applicants or candidates with disabilities can submit a reasonable accommodation request at any point in the hiring process, and MCCCD’s policy is to provide reasonable accommodation unless such accommodation would cause an undue hardship. If a reasonable accommodation is needed, please contact the ADA Coordinator from the hiring campus location for assistance via email at [email protected]
MCCCD is an Equal Opportunity Employer.
Posting Close Date
First review October 26, 2026
Applications received after first review date may not be reviewed
EEO, Title IX, & Clery Act
Maricopa County Community College District (MCCCD) will not discriminate, nor tolerate discrimination in employment or education, against any applicant, employee, or student because of race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship status (including document abuse), age, disability, veteran status or genetic information.
Title IX of the Education Amendments of 1972, states: "No person in the United States shall, on the basis of sex, be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any education program or activity receiving Federal financial assistance."
The policy of the MCCCD is to provide an educational, employment, and business environment free of gender discrimination. Incidents of misconduct should be reported to the college Title IX Coordinator, as outlined in policy, contact information is available at this link Title IX Coordinators.
The Clery Act is a Federal law requiring United States Colleges and Universities to disclose information about crime on and around their campuses. Crime reporting data for each of the Maricopa Community Colleges, as required under the Clery Act, is available at this link Clery Act.
Salary Context
This $104K-$135K range is in the lower quartile for AI Engineering Manager roles in our dataset (median: $202K across 7 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At District Support Services Center, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $251,622 based on 55 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 52% below the category median. Disclosed range: $104K to $135K.
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.
District Support Services Center AI Hiring
District Support Services Center has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Tempe, AZ, US. Compensation range: $135K - $135K.
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 Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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