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About This Role
Position Summary
The Business Systems \& AI Manager serves as the organization's internal expert on enterprise technology systems, data, and emerging AI capabilities. Reporting to the Chief Financial and Administrative Officer, this position is responsible for understanding how the Minnesota JCC's software platforms function individually and collectively, ensuring they are optimized, integrated, and aligned with organizational priorities.
This individual will lead the organization's digital innovation and AI strategy, helping departments leverage technology responsibly to improve efficiency, decision\-making, and the employee experience. The Business Systems \& AI Manager will also champion data accessibility and reporting, empowering staff across the organization to make informed decisions through meaningful analysis.
Success in this role requires an independent thinker who enjoys solving complex problems, simplifying processes, challenging existing practices, and helping employees with varying technical abilities confidently utilize technology. This individual will influence organizational change through expertise, collaboration, and thoughtful implementation rather than direct authority.
This is a newly created position designed to help the Minnesota JCC maximize the value of its technology investments while preparing the organization for the future.
Essential Functions
This job description is not intended to cover or contain a comprehensive listing of activities, duties or responsibilities required of the employee in this position. Activities, duties and responsibilities may change at any time with or without notice. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions of this position.
Enterprise Systems Leadership
- Serve as the organization's subject matter expert for core business software platforms.
- Develop a comprehensive understanding of system functionality, integrations, workflows, and dependencies.
- Identify opportunities to optimize existing systems and ensure they work effectively together.
- Evaluate current business processes and recommend improvements that increase efficiency, reduce redundancy, and improve the employee experience.
- Partner with departments to ensure technology solutions align with operational goals.
Digital Innovation and AI Strategy
- Lead the development and implementation of the Minnesota JCC's digital innovation and AI strategy.
- Evaluate AI platforms and recommend organization\-wide standards, governance, and best practices.
- Identify practical AI use cases that improve productivity, customer service, communication, and operational effectiveness.
- Stay informed about emerging technologies and recommend innovations that enhance the employee and member experience.
- Develop policies and training that support responsible, secure, and ethical use of AI.
- Foster a culture of continuous learning and innovation by helping staff confidently adopt new digital tools and technologies.
Data and Reporting
- Develop expertise in reporting capabilities across all major software platforms.
- Create dashboards, reports, and analytical tools that support organizational decision\-making.
- Train and coach staff in accessing, interpreting, and utilizing data effectively.
- Partner with leadership to identify key performance indicators and reporting needs.
- Support leaders in transforming data into actionable insights that improve operations and strategic planning.
Training and Change Management
- Design and deliver technology training for employees with diverse learning styles and varying levels of technical proficiency.
- Create user\-friendly documentation and learning resources.
- Serve as a trusted resource for departments implementing new technology or workflows.
- Promote adoption of new tools through education, coaching, and ongoing support.
- Build confidence in technology across the organization by making systems more approachable and accessible.
Technology Improvement
- Continuously assess the organization's technology ecosystem.
- Recommend technologies or process improvements that simplify work and eliminate unnecessary complexity.
- Identify systems, processes, or practices that should be streamlined, consolidated, or discontinued to improve organizational effectiveness.
- Collaborate with vendors and internal stakeholders to maximize technology investments.
- Develop recommendations that improve system integration, reduce manual work, and increase organizational efficiency.
Minimum Qualifications
Required
- Bachelor's degree in Information Systems, Business Administration, Computer Science, Data Analytics, or a related field, or equivalent professional experience.
- Three to five years of experience managing or optimizing enterprise software systems.
- Experience implementing or supporting software integrations.
- Experience developing reports, dashboards, and data analysis tools.
- Demonstrated experience leading technology or process improvement initiatives.
- Strong analytical, organizational, and project management skills.
- Excellent communication and relationship\-building abilities.
- Ability to work independently, prioritize multiple initiatives, and drive projects from concept through implementation.
- Experience working with employees who have diverse learning styles and varying levels of technical proficiency.
Preferred
- Experience with nonprofit, membership\-based, CRM, ERP, HRIS, financial management, or recreation management systems.
- Experience implementing or governing AI platforms within an organization.
- Familiarity with Microsoft Power Platform, Power BI, SQL, API integrations, or similar business intelligence tools.
- Experience delivering technology training to adult learners.
- Knowledge of cybersecurity best practices related to enterprise software and AI.
Competencies
- Systems Thinking
- Digital Innovation
- Artificial Intelligence Strategy
- Technology Integration
- Data Analysis and Business Intelligence
- Process Improvement
- Change Management
- Project Management
- Collaboration and Influence
- Customer Service
- Continuous Improvement
Performance Expectations
Within the first 12–18 months, the Business Systems \& AI Manager will:
- Develop a comprehensive inventory of the organization's technology platforms, integrations, and data flows.
- Create a multi\-year Business Systems and Digital Innovation Roadmap that identifies opportunities to optimize, consolidate, replace, or retire systems.
- Establish organizational standards and governance for the responsible use of artificial intelligence.
- Improve reporting capabilities by developing executive dashboards and standardized reports that support strategic decision\-making.
- Increase employee adoption and effective utilization of core technology platforms through training, documentation, and ongoing coaching.
- Identify measurable efficiencies by simplifying workflows, reducing manual processes, and recommending practices the organization should discontinue.
- Build collaborative partnerships across departments, becoming a trusted advisor for technology, data, process improvement, and digital innovation.
Abuse Risk Management
- Adhere to policies related to boundaries with participants
- Attend required abuse risk management training
- Adhere to procedures related to managing high risk activities and supervising participants
- Report inappropriate behaviors and policy violations
- Follow mandated abuse reporting requirements
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to verbally communicate with others. In the office setting, this is primarily a sedentary role, which requires the employee to sit at a desk for consecutive hours at a time using a computer or other office equipment. Additional physical requirements include occasional bending, crouching, reaching and lifting.
Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions of this position.
Working Relationships
This position reports to the Chief Financial and Administrative Officer and partners closely with leaders and staff across all departments. Success requires the ability to build strong relationships, influence without direct authority, and serve as a trusted advisor in technology adoption, digital innovation, and organizational improvement.
Compensation
The salary range is $75,000–$82,000 annually, commensurate with experience.
About Us
The Minnesota JCC provides meaningful programs and services for people of all backgrounds, ages, interests and abilities that promote well\-being, foster inclusivity, and strengthen the entire community. Guided by Jewish values and culture, our programs include Early Childhood Educations, Summer Camps, Health \& Wellness, Recreation, Youth \& Teem Programing, Adult Enrichment, Inclusion Programming, Senior Supportive Services, Jewish Arts \& Culture, and Special Events.
Our Culture
The Minnesota JCC is guided by Jewish values including community, lifelong learning, inclusion, innovation, and caring for one another. We welcome candidates from all backgrounds and experiences.
You bring—or are excited to develop—a deep appreciation for Jewish culture and values, which form the foundation of our community.
The Minnesota JCC is an Equal Opportunity Employer committed to building an inclusive workplace where all employees feel valued, respected, and empowered to contribute their unique talents.
Our Values
The J is for Everyone
*We create spaces and opportunities where people feel safe, seen, and connected. When everyone belongs, our entire community grows stronger.*
We Adapt for the Greater Good
*We are rooted in purpose and flexible in approach. We are responsive to today and ready for tomorrow, even when the path ahead isn’t completely clear.*
We Show Up Strong
*We meet the moment. We take pride in what we deliver and how we deliver it. We aim high, prepare, and follow through, because people are counting on us.*
EEOC Statement
The Minnesota JCC provides equal opportunity to employees and applicants for employment in accordance with applicable laws. Personnel decisions are made based on merit and the needs of the organization. The Minnesota JCC does not discriminate against any employee or applicant for employment because of race, color, creed, religion, national origin, sex, marital status, familial status, status with regard to public assistance, disability, genetic information, sexual orientation, gender identity, gender expression, age, military or veteran status, membership or activity in a local human rights commission, or any other status protected by law.
Salary Context
This $75K-$82K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Minnesota Jewish Community Center, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($78K) sits 64% below the category median. Disclosed range: $75K to $82K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Minnesota Jewish Community Center AI Hiring
Minnesota Jewish Community Center has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saint Paul, MN, US. Compensation range: $82K - $82K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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