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Overview:
About Boyne Resorts
Boyne Resorts is a privately held, family\-owned ski company with a 75\-year legacy of creating transformative outdoor lifestyle experiences across North America. As a “company of resorts” rather than a “resort company,” we honor the unique identity of each property while sharing a commitment to excellence — and to serving our people, our guests, and our communities first.
Our shared culture is built on the L.E.A.D.S. values: Long\-Term Thinking, Excellence in Execution, Attitude is Everything, Develop Great People, and Serve First.
Position Summary
The Vice President, Enterprise AI leads the responsible, high\-value adoption of artificial intelligence across all Boyne Resorts corporate functions and resort properties — converting AI from isolated experimentation into a durable, measurable enterprise capability. This is a lean, matrixed leadership role: the VP operates the AI Center of Excellence, partners with resort IT and function leaders, cultivates a federated network of AI champions, and serves as the operational engine of the AI Steering Committee, translating executive strategy into adoption, value, and disciplined governance.
The role's scope expands in phases — beginning with internal adoption and productivity across corporate and all resort properties, then extending into guest\- and customer\-facing AI (personalization, marketing, revenue optimization) as the function matures. Ownership of the underlying data platform and ML engineering build remains with IT/Data; this role sets direction and drives adoption of the capabilities built on top of it.
Responsibilities:
WhatYou’llDo
Strategy \& Roadmap
- Own the enterprise AI strategy and multi\-year roadmap in service of the AI Steering Committee, maintaining a prioritized portfolio of use cases and an enterprise view of AI investment, ROI, and total cost of ownership.
Adoption \& Enablement
- Drive and measure AI adoption across corporate functions and all resort properties, operating the AI Center of Excellence and Skills Registry as the enterprise hub for reusable capability.
- Build and lead a federated network of AI champions, and own enablement — training, playbooks, prompt libraries, and use\-case showcases — that build organization\-wide AI fluency.
- Define and track adoption and value metrics, including active usage, use cases in production, realized productivity, and sentiment.
Governance, Risk \& Responsible AI
- Operate day\-to\-day AI governance under the AI Steering Committee and IT Steering Committee frameworks, stewarding model\-selection, seat\-lifecycle, spend, and use\-case intake standards.
- Partner with Security, Legal, and HR on responsible\-AI principles, data protection, and acceptable use, and own vendor relationships and cost optimization across AI providers.
Value Delivery, Partnership \& Leadership
- Partner with function and resort leaders to surface and capture high\-ROI opportunities, and lead phased guest/customer\-facing AI initiatives with the CMO and COO.
- Lead through influence across a matrixed, multi\-property organization, building coalitions between corporate and the field and communicating adoption, value, and risk to the AI Steering Committee, CEO, and ownership/board in business terms.
Qualifications:
WhatWe’reLooking For
- Bachelor’s degree required; an advanced degree or relevant certification (e.g., enterprise architecture, change management, AI governance) is a plus.
- 10\+ years of progressive technology, digital, or transformation leadership, including several years in AI, data, or enterprise digital transformation.
- A demonstrated track record driving enterprise\-wide technology adoption and change at scale, ideally across a multi\-site or distributed\-workforce organization.
- Fluency in large language models, the enterprise AI vendor landscape, and AI risk/governance — credible with IT without needing to be a hands\-on ML engineer.
- Formal change\-management experience and the executive presence to communicate adoption, value, and risk to senior leadership, ownership, and the board.
- Strong vendor\-management and financial acumen (ROI, TCO, license optimization); hospitality, multi\-unit retail, or distributed\-workforce experience preferred.
Job Benefits
This position is regular full\-time and eligible for:
- Health, dental, and vision insurance programs
- Flexible spending account programs
- Life insurance programs
- Paid time off and paid leave programs
- 401(k) savings plan
- Employee ski pass and dependent ski passes
- Other company perks
Equal Opportunity
Boyne Resorts is an equal opportunity employer (Minority/Female/Disabled/Veteran) and participates in E\-Verify.
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 Boyne Resorts, 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.
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.
Boyne Resorts AI Hiring
Boyne Resorts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Petoskey, MI, US.
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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