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About This Role
Our Story:
Crisis Prevention Institute Inc. (CPI) is the worldwide leader in evidence-based de-escalation and crisis prevention training and dementia care services. Our programs teach professionals the skills to recognize, prevent, and respond to crises in the workplace. Since 1980, we’ve helped train more than 17 million people within service-oriented industries including education, health care, behavioral health, long-term care, human services, security, corporate, and retail.
At CPI, we are dedicated to changing behaviors and reducing conflict for the Care, Welfare, Safety, and Security of everyone. We believe the power of empathy, meaningful connections, personal safety, and security are the antidotes to fear and anxiety. It’s a philosophy that is central to everything we do, and traces back to our beginning.
As a member of the team, you can expect to:
- Make a difference through your work – You’ll be proud to tell your family and friends about what you do.
- Gain significant career experience only obtained within a fast-growing organization – Entry-level roles through executive leadership.
- Feel fulfilled and have fun – We work hard but make the time to build meaningful relationships and celebrate the wins.
The Role:
The Director leads CPI’s enterprise AI platforms, governance, and responsible adoption while helping employees get more value from the software CPI already owns. This hands-on technology leader establishes practical controls, administers approved AI services, evaluates integrations and agents, and builds learning programs that improve how teams work across the organization.
The role partners with Technology, Information Security, Data, Legal, Finance, HR, and business leaders. It combines platform operations and financial stewardship with teaching, coaching, and change management. Success is measured by safe and effective adoption, employee capability, reduced duplication, and business outcomes—not by the number of training sessions delivered.
What You Get To Do Everyday:
AI Strategy and Governance
- Develop an enterprise AI roadmap aligned with business needs and CPI’s technology strategy; translate emerging capabilities into practical recommendations and measurable use cases.
- Own day-to-day AI governance processes and coordinate the AI Governance Committee, including intake, evaluation, approval, documentation, and lifecycle standards for platforms, models, agents, connectors, and use cases.
- Partner with Information Security, Legal, Privacy, HR, and Data on security, data access, retention, intellectual property, acceptable use, and applicable regulatory and contractual requirements; maintain evidence for review and audit.
- Define responsible experimentation and escalation paths so employees can use approved AI capabilities confidently.
AI Platforms, Integrations and Financial Management
- Serve as operational owner for approved enterprise AI platforms, including administration of licensing, users, access, configuration, models, connectors, agents, and controls.
- Evaluate, configure, test, deploy, support, and retire AI features, integrations, and automations; prototype use cases and partner with Engineering and application owners when deeper development is needed.
- Establish standards for agent ownership, testing, security review, monitoring, support, and retirement; troubleshoot access, configuration, and integration issues.
- Track AI licensing, subscriptions, token or consumption costs, utilization, and vendor performance; forecast spend, optimize licenses, and support renewals with Finance and Procurement.
Digital Adoption and Employee Learning
- Build an enterprise program that teaches employees how and when to use existing applications, including Microsoft 365, Teams, SharePoint, Copilot, CRM, collaboration, analytics, and other approved tools.
- Assess underused features and skill gaps with application owners and business teams; develop role-based learning paths, live and virtual workshops, demonstrations, office hours, coaching, short videos, and self-service guides.
- Develop AI literacy and practical responsible-use training for executives, leaders, employees, and specialized functions, including prompting, data handling, approved tools, and relevant workflows.
- Establish a network of AI and technology champions who share proven practices and help their teams adopt new ways of working.
- Partner with HR and Learning and Development on employee skills and with project teams on communications, readiness, training, and adoption after launch.
Business Partnership and Value Realization
- Work with departments to understand how work is performed, identify friction and manual steps, and demonstrate solutions using current platforms, automation, or AI.
- Identify existing capabilities before new software is purchased; advise Technology and business leaders where current tools can address a stated problem and where a new investment is warranted.
- Measure active usage, feature adoption, employee confidence, AI consumption, costs, workflow improvements, and documented business outcomes; report trends and recommendations to leadership.
- Gather employee feedback and use it to improve platforms, learning materials, support, and change plans.
You Need to Have:
- Bachelor's Degree
- Seven or more years in enterprise technology, platform operations, digital adoption, technology learning, organizational change, or a related field, with substantial hands-on enterprise technology experience.
- Experience implementing or administering enterprise AI, SaaS, automation, or comparable emerging technology platforms, including access, licensing, configurations, and support.
- Working knowledge of generative AI, models, agents, APIs, connectors, data protection, identity, security, and responsible-use controls.
- Demonstrated ability to design and deliver engaging technology training and adoption programs for varied audiences, from individual contributors to executives.
- Ability to connect business workflows to technology capabilities and evaluate usage, costs, adoption, and value.
- Strong facilitation, writing, presentation, analytical, and cross-functional partnership skills; ability to operate independently in an evolving area.
We'd Love to See:
- Experience with Microsoft 365, Copilot, Copilot Studio, Power Platform, ChatGPT Enterprise, Claude Enterprise, ServiceNow, or similar platforms.
- Experience with enterprise AI governance, agent development, AI integrations, digital adoption platforms, learning systems, and change management practices.
- Experience building usage dashboards, champion networks, executive reporting, or FinOps processes.
What We Offer:
- Comprehensive compensation package
- Comprehensive benefits package
- 401k
- PTO
- Health & Wellness Days
- Paid Volunteer Time Off
- Continuing education and training
- Hybrid / Remote work schedule
- Paid Parental Leave
Crisis Prevention Institute is an Equal Opportunity Employer that does not discriminate against any applicant or employee on the basis of age, race, color, ethnicity, national origin, citizenship, religion, creed, sex, sexualorientation, gender, gender identity, or expression (including against any individual that is transitioning, hastransitioned, or is perceived to be transitioning), marital status or civil partnership/union status, physical ormental disability, medical condition, pregnancy, childbirth, genetic information, military and veteran status, orany other basis prohibited by applicable federal, state, or local law. The Company will consider foremployment qualified applicants with criminal histories in a manner consistent with local and federalrequirements. Our management team is dedicated to this policy with respect to recruitment, hiring, placement,promotion, transfer, training, compensation, benefits, employee activities, and general treatment duringemployment.
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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 Crisis Prevention Institute, 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. Director-level AI roles across all categories have a median of $272,300.
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.
Crisis Prevention Institute AI Hiring
Crisis Prevention Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, 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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