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About This Role
Company Description UFG, Inc.
Director of AI \& Technology Operations
Imagine a career where your passion meets purpose, and your work has a global impact. At United Franchise Group, we're not just offering jobs\-we're offering the chance to be part of something bigger. We're looking for individuals who want to innovate, inspire, and lead in a company that's shaping the future of franchising worldwide.
For nearly 40 years, UFG has been at the forefront of franchising, growing from a small team to an international powerhouse with over 1800 locations in 60 countries. But we're just getting started, and we want you to be a part of our next chapter. Whether you're just starting out or looking to take your career to new heights, UFG offers positions at every level, with the tools and support to help you succeed.
Our headquarters in West Palm Beach, FL is more than just an office\-it's a hub of creativity, collaboration, and community. Here, you'll find a culture that celebrates diversity, encourages innovation and rewards hard work. From day one, you'll be surrounded by people who are passionate about what they do and driven to make a difference.
This is more than a career\-it's a calling. At UFG, we believe in taking care of our team as they take care of our franchisees. That's why we offer competitive pay, comprehensive benefits, and perks that go beyond the ordinary.
Are you ready to make your mark? Join us at UFG and be part of a team that's changing the world, one franchise at a time.
Job Description
The Director of AI \& Technology Operations is responsible for driving AI adoption, managing AI\-powered platforms, and overseeing the operational delivery of technology initiatives across UFG brands, staff, and franchisees. This role combines structured program management with hands\-on AI fluency — translating executive AI vision into deployed capabilities within Microsoft Copilot, Power Apps, Gorilla Dash, RevScale, NetSuite and internal development projects. The Director bridges strategy and execution, ensuring AI initiatives are delivered on time, governed properly, and adopted at scale while reducing hands\-on CTO execution dependency.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
*AI Strategy \& Adoption*
- Own and execute UFG's AI adoption roadmap across all brands, identifying high\-impact use cases and building structured rollout plans aligned with business priorities.
- Lead the advancement and expansion of Microsoft Copilot across UFG departments — driving adoption, training, prompt engineering best practices, and measurable productivity gains for corporate staff and franchisees.
- Design, build, and deploy Microsoft Power Apps solutions and AI\-powered automations that streamline operations for UFG staff and franchise owners.
- Evaluate and integrate emerging AI tools and platforms (LLMs, agentic workflows, automation platforms) into existing UFG systems and processes.
*Platform \& Product Management*
- Serve as the primary AI and technology operations liaison between UFG and Gorilla Dash — coordinating CRM feature development, API integrations, and AI\-enhanced capabilities for franchisee\-facing systems.
- Collaborate with RevScale on AI\-driven sales, marketing, and franchise development tools — ensuring alignment with UFG's brand and operational standards.
- Work directly with UFG's internal Development team to scope, prioritize, and deliver AI\-focused features, integrations, and platform enhancements across the tech stack.
- Manage the lifecycle of AI\-focused applications — from discovery and MVP through production deployment, user training, and iterative improvement.
*Program Management \& Execution*
- Drive cross\-functional delivery of technology initiatives across Development, IT/Security, ERP, Brand Technology, and external vendors using disciplined program management frameworks (PMP/Agile/Scrum).
- Establish operational cadence: sprint planning, dependency tracking, milestone reporting, risk escalation, and executive\-ready status updates for AI and tech projects.
- Coordinate multi\-team initiatives including platform migrations, enterprise tool rollouts, and vendor integrations with clear timelines and accountability.
- Provide structured executive summaries and AI ROI reporting to the CTO, COO, and executive leadership.
*Governance \& Vendor Coordination*
- Own AI governance frameworks — responsible use policies, data handling standards, and compliance alignment for all AI deployments.
- Lead vendor evaluations, renewals, and performance management for AI and technology partners.
- Act as an operational backup to the CTO on technology operations matters.
COMPETENCIES:
- AI Fluency — Practical understanding of LLMs, conversational AI, automation platforms, and enterprise AI deployment.
- Program Leadership — Ability to manage complex, multi\-stakeholder initiatives from strategy through execution with measurable outcomes.
- Stakeholder Orchestration — Skilled at aligning cross\-functional teams (Dev, IT, ERP, Marketing, Franchise Operations) around shared delivery goals.
- Critical Thinking / Execution — Translates ambiguity into structured plans and drives them to completion.
- Communication — Executive\-ready reporting, training facilitation, and franchisee facing communication.
Qualifications QUALIFICATIONS:
- 5\+ years of experience in technology program management, AI product delivery, or enterprise digital transformation.
- + Demonstrated experience deploying AI tools (Copilot, Power Apps, LLM\-based products, or similar) in operational business environments.
+ Strong understanding of CRM platforms, SaaS product management, and API\-driven integrations.
+ Proven ability to lead cross\-functional teams and manage vendor relationships.
+ Experience in franchise, multi\-unit, or distributed business environments is a strong plus.
EDUCATION AND/OR EXPERIENCE (PREFERRED):
- + Bachelor's degree or equivalent combination of education and experience.
- • PMP (Project Management Professional) or Certified ScrumMaster (CSM) — strongly preferred. • Microsoft certifications (Power Platform, Azure AI Fundamentals, Copilot) — preferred.
- + AI/ML executive education (MIT, Stanford, or equivalent) — a plus.
+ Experience with Salesforce, NetSuite, Azure DevOps, or similar enterprise platforms.
Additional Information *Once you become part of our amazing team of winners you’ll enjoy:*
- Competitive compensation
- Comprehensive training to hone your skills at our headquarters
- Employee Development Programs
- Medical, Dental, Vision, and Life insurance coverage
- Short\- and Long\-term disability insurance
- Generous time off \& paid holidays
- 401(k) plan with company match
- Social gatherings and team building activities
- Leadership workshops for personal development
- Recognition for our top performers
- Philanthropy – a chance to give back to the community
*Join us at United Franchise Group – a global leader for entrepreneurs!*
*Apply today!*
All your information will be kept confidential according to EEO guidelines.
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 United Franchise Group, 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. Director-level AI roles across all categories have a median of $272,150.
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.
United Franchise Group AI Hiring
United Franchise Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Palm Beach, FL, US.
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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