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SCCMHA JOB VACANCY ANNOUNCEMENT
CLASSIFICATION: AI Governance Program Coordinator
PAY RANGE: $31\.53 \- $39\.04 per hour
POSITION SUMMARY:
This position serves as the operational administrator and coordinator of SCCMHA's Artificial Intelligence (AI) Governance Program. The AI Governance Program Coordinator is responsible for coordinating, documenting, monitoring, auditing, reporting, and supporting all activities related to SCCMHA's AI Governance Framework, AI Governance Committee (AIGC), AI Registry, AI risk management processes, and approved AI use cases.
Working within SCCMHA's Quality, Compliance, Information Technology, Information Security, and Data Analytics programs, this position facilitates the AI use case intake and review process, coordinates risk assessments and governance reviews, maintains official governance records, supports AI monitoring and audit activities, and assists with workforce education and awareness initiatives. The position acts as the central coordination point between requestors, reviewers, leadership, and the AI Governance Committee.
The AI Governance Program Coordinator promotes the responsible, ethical, secure, transparent, and compliant use of artificial intelligence technologies in alignment with SCCMHA's AI Governance Framework, AI Governance \& Acceptable Use Policy, HIPAA requirements, Joint Commission guidance, Coalition for Health AI (CHAI) Responsible Use of AI in Healthcare (RUAIH) guidance, and applicable federal and state regulations.
In addition to AI governance responsibilities, this position serves as a key operational support role within the Compliance Department, assisting with compliance monitoring, auditing, reporting, investigations, training, and regulatory readiness activities.
This position works in a structured team environment and has responsibilities unique to the teams in which they belong. These teams have Primary, Secondary and Supporting members. It is required that the Primary and Secondary members of these Teams be aware of the status of all projects and major initiatives with enough knowledge to discuss with the Chief Information Officer and others at the time of the inquiry. All Primary and Secondary members of the Team must discuss, vet, and agree upon any changes to processes, procedures, standards, equipment, and infrastructure that relate to the responsibilities of the team. All changes must follow a change control process which includes scope and risk analysis, team and peer review, contingency and backup planning, pre\-deployment testing and validation, implementation, testing, and documentation in the change control log. Root cause analysis will be performed for all process, network and system failures or disruptions and corrective actions will be put in place. All members of the team will be proactive by constantly looking for innovative techniques and technologies that will improve the effectiveness, efficiency, longevity, and sustainability in all areas of responsibility. Primary members must act as the lead on all projects, tasks, and initiatives with the full involvement of the Secondary members. Primary members will act as mentors whose goal is to assist all Secondary members with becoming a SME (Subject Matter Expert) in all aspects within the team. Supporting members of the team will assist the Primary and Secondary members within their areas of expertise to fulfill the responsibilities of the team.
As a Staff Member within the Compliance Department, this person will seek to become knowledgeable in all areas within the Department by observing and allowing Senior members of the team to mentor them. As a Primary Member of the AI Governance Team, this position facilitates overall AI governance functions, provides project management and administrative support, coordinates governance activities, and assists with all AI governance initiatives throughout the organization. As a Secondary Member of the Compliance Team, this position supports SCCMHA’s compliance program operations and assists the Chief Compliance \& Quality Officer in maintaining an effective, agency wide compliance system. As a Secondary Member of the BI Reporting Team, this position may prepare reports in financial, graphical and dashboard formats for presentation to groups and report submissions. Prepare data for reports and submission files to various external agencies, partners, and regulatory authorities. Assess internal agency reporting needs for administrative and clinical areas.
This position will be knowledgeable about and actively support culturally competent recovery\-based practices; person centered planning as a shared decision\-making process with the individual, who defines his/her life goals and is assisted in developing a unique path toward those goals; and a trauma informed culture of safety to aid consumer in the recovery process.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Primary Member of the AI Governance Team
AI Governance Program Administration
1\. Serves as Lead Coordinator and Liaison for the SCCMHA AI Governance Program.
2\. Coordinates the AI Use Case Intake and Review Process, including receiving submissions, assigning tracking numbers, coordinating required multidisciplinary reviews, monitoring completion, documenting decisions, and escalating delays.
3\. Maintains the official SCCMHA AI Registry and related governance records, including approved use cases, risk classifications, monitoring plans, reassessments, incidents, approvals, conditions, remediation activities, committee decisions, and audit evidence.
4\. Maintains AI governance policies, procedures, forms, templates, workflows, and documentation.
5\. Coordinates vendor AI governance reviews and assists with AI\-related procurement, contract review, BAA verification, and vendor risk management activities.
6\. Coordinates annual AI governance reviews, reassessments, maturity evaluations, effectiveness reviews, metrics analysis, policy reviews, and preparation of the annual AI Governance Report and Plan.
7\. Develops and maintains AI governance dashboards, performance indicators, trend analyses, AI Registry reports, and executive, committee, audit, accreditation, and regulatory reports.
Governance Committee Coordination
1\. Provides administrative coordination for the AI Governance Committee, including agendas, minutes, dashboards, reports, action items, decision tracking, follow\-up activities, and communications among committee members, subject matter experts, and requestors.
AI Review and Risk Assessment Coordination
1\. Coordinates required AI use case reviews, assessments, risk tier determinations, mitigation activities, corrective action plans, and supporting documentation.
2\. Assigns and tracks Privacy, Compliance, Security, Clinical, Quality, IT, Procurement, HR, and Legal reviews as required by risk tier.
Monitoring, Auditing, and Compliance
1\. Coordinates ongoing monitoring, auditing, reassessments, risk mitigation activities, corrective action plans, incident tracking, complaint intake, investigation support, remediation tracking, and compliance reviews for approved AI use cases.
2\. Assists with AI\-related HIPAA, compliance, security, privacy, quality, accreditation, certification, and regulatory reviews.
3\. Escalates privacy, security, safety, compliance, bias, or operational concerns to the Executive Sponsor and AI Governance Committee as appropriate.
Workforce Education and Awareness
1\. Supports AI governance communications, workforce awareness campaigns, training activities, educational materials, guidance documents, and required training completion tracking.
2\. Monitors workforce compliance with SCCMHA AI Governance and Acceptable Use requirements and assists with investigations involving unauthorized or non\-compliant AI use.
Secondary Member of the Compliance Team
1\. Supports compliance monitoring, auditing, investigations, reporting, documentation, corrective actions, follow\-up activities, and regulatory readiness.
2\. Prepares compliance dashboards, reports, metrics, and documentation for internal and external reviews.
3\. Assists with policy and procedure development, revision, and maintenance.
4\. Supports compliance training, workforce education, and awareness initiatives.
5\. Provides project management and data analytic support for compliance initiatives.
6\. Assists with accreditation, certification, and regulatory readiness activities, including Joint Commission, CARF, CCBHC, and Medicaid.
7\. Maintains compliance records, logs, and documentation in accordance with regulatory requirements.
8\. Coordinates with Quality, Privacy, Information Security, and other departments on cross\-functional compliance activities.
INCIDENTAL DUTIES AND RESPONSIBILITIES:
1\. Assists the CIO\|CQCO with AI governance policy and procedure writing and maintenance.
2\. Participates in AI\-related committees, workgroups, and governance meetings.
3\. At the discretion of the CIO\|CQCO, may be an indirect report to other agency directors for specific tasks as assigned.
4\. Attends mandated SCCMHA regulatory staff training.
5\. Attends meetings both in\-person and remotely; presents to groups, facilitates meetings, creates agendas, maintains minutes, and performs needed project management tasks.
6\. Attends meetings, seminars, workshops, and community events related to the public mental health mission and training sessions to maintain or upgrade current knowledge and skills required by this position and to maintain professional proficiency.
7\. Communicates project or work status to the CIO\|CQCO and other department leaders regularly as appropriate.
8\. May be required to commute to other SCCMHA facilities and business partners and vendor locations, when necessary, to investigate and resolve problems, implement new systems, train staff, etc.
9\. May represent SCCMHA on state or regional committees and workgroups.
10\. May serve as a member of various community committees, which promote the general goals of SCCMHA.
11\. Must react productively and positively to change and handle other essential tasks as assigned.
12\. Perform other duties as assigned by the CIO\|CQCO.
13\. Reads journals, periodicals, and research subjects on the Internet to increase job related knowledge and further professional and talent advancement.
14\. Serves in a team setting approach by backing up other department personnel in their duties when needed.
15\. Works closely with and coordinates efforts with all other agency staff and leadership as needed.
16\. Works closely with and coordinates efforts with all Quality, Compliance, Business Intelligence, Information Security, Privacy, and Information Technology staff.
17\. Assists the CIO\|CQCO with compliance policy development, maintenance, and review.
18\. Supports compliance investigations, documentation, and follow‑up activities.
19\. Participates in compliance committees, workgroups, and regulatory readiness meetings.
20\. Provides backup support for compliance reporting, auditing, and monitoring tasks.
(The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all duties and responsibilities required of personnel so classified.)
REPORTING RELATIONSHIPS:
Reports to: CIO\|CQCO
Supervises: None
WORKING CONDITIONS/ENVIRONMENT:
1\. Daily exposure in all other department areas while working with staff at their workstation locations.
2\. It is not unusual to work varied and extra hours to complete assignments to meet deadlines.
3\. May perform on\-call job duties for after\-hour support coverage at the discretion of the Chief Information Officer.
4\. Occasional exposure to consumers with potential for disruptive, aggressive behavior and communicable diseases.
5\. Occasionally drives personal automobile on agency business to offsite facilities or meetings, sometimes in bad weather.
6\. Works at workstation using keyboard and viewing computer screen for long periods.
7\. Works in typical professional office environment with pressures of time constraints, multiple projects, priorities, and numerous interruptions from telephone calls and walk\-ins.
QUALIFICATIONS:
Education: Bachelor’s degree in Healthcare Administration, Public Health, Health Information Management, Business Administration, Applied Science, Computer Science, Computer Information Systems, Quality Management, Compliance, Information Security, Data Analytics, or another closely related field. A combination of skills, education, and experience that meets organizational needs may be considered.
Experience:
Minimum: Three (3\) years’ relevant experience in healthcare quality, compliance, AI governance, project management, privacy, security, healthcare technology, information management, project coordination, risk management, governance, auditing, information technology, or related field.
Preferred: Five (5\) years’ experience in healthcare quality, compliance, AI governance, project management, privacy, security, healthcare technology, information management, project coordination, risk management, governance, auditing, information technology, or related field.
One (1\) year experience:
1\. Coordinating committees, projects, governance programs, workgroups, or organizational initiatives.
2\. With healthcare regulatory requirements.
3\. Preparing reports, dashboards, performance metrics, presentations, and committee materials.
4\. Working with behavioral health, healthcare, Medicaid, HIPAA, accreditation programs, compliance, or regulatory programs.
5\. Project management, business analysis, and data compilation.
6\. Professional administrative or clinical experience working with behavioral health populations.
7\. Experience supporting healthcare compliance programs, investigations, audits, or regulatory reviews.
Licenses and Credentials: Valid Michigan Driver’s license with good driving record.
Preferred Certifications/Knowledge
1\. Certified in Healthcare Compliance (CHC)
2\. Project Management Professional (PMP)
3\. Healthcare AI Governance, Privacy, Security, or Data Governance training
Knowledge, Skills, and Abilities:
1\. Knowledge of HIPAA privacy and security requirements.
2\. Strong project coordination and organizational skills.
3\. Ability to lead and manage projects.
4\. Strong analytical and problem\-solving abilities.
5\. Excellent written and verbal communication skills.
6\. Ability to manage multiple priorities and deadlines.
7\. Ability to maintain detailed documentation and records.
8\. Ability to facilitate meetings and coordinate multidisciplinary teams.
9\. Proficiency with Microsoft Office, Teams, Excel, and reporting tools.
10\. Excellent analytical, problem solving and critical thinking skills.
11\. Solid troubleshooting and communication skills.
12\. Ability to demonstrate exceptional customer service skills in working with other staff, contractors, and vendors.
13\. Ability to train and assist others.
14\. Ability to exercise mature judgment and maintain strict confidentiality.
15\. Ability to maintain favorable interpersonal working relationships and positive public relations.
16\. Ability to plan and organize work, perform tasks consistently and adhere to priorities.
17\. Ability to produce accurate and comprehensive work products with minimal direction.
18\. Ability to provide small group leadership or management.
19\. Professional level verbal and written communication skills.
20\. Ability to learn emerging AI technologies and governance requirements.
Preferred:
1\. Knowledge of healthcare compliance, quality improvement, and governance principles.
2\. Knowledge of AI governance concepts, risk management, and responsible AI practices.
3\. Familiarity with data visualization.
4\. Knowledge of mental health services.
5\. Knowledge of Joint Commission, CARF, CCBHC, CHAI RUAIH, NIST AI RMF, and Medicaid regulatory requirements.
Physical/Mental Requirements:
1\. Ability to handle stress in meeting deadlines and dealing with large numbers of employees and/or consumers.
2\. Ability to lift boxes and equipment weighing up to 10 pounds; carry, climb, stoop, bend, walk, stand, and sit for extended periods of time.
3\. Ability to plan short and long range and to manage and schedule time.
4\. Hearing acuity to converse in person and on telephone.
5\. Ability to operate standard office equipment, including a PC, keyboard, copy machine, fax machine, and related equipment.
6\. Mental capacity to think independently, follow instruction and use judgment.
7\. Strong interpersonal skills to interact with leadership, employees, consumers, and the public.
8\. Visual Acuity to read and proofread documents.
(Listed qualifications are for guidance in filling this position. Any combination of education and experience that provides the necessary knowledge, skills, and abilities will be considered; however, mandatory licensing or certification requirements cannot be waived. Physical/mental requirements cannot be waived unless specifically indicated.)
Salary Context
This $64K-$81K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Saginaw County Community Mental Health Authority, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($72K) sits 66% below the category median. Disclosed range: $64K to $81K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Saginaw County Community Mental Health Authority AI Hiring
Saginaw County Community Mental Health Authority has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saginaw, MI, US. Compensation range: $81K - $81K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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