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VP of Innovation & AI
Parasol Alliance | Remote | Full-Time | ~30% Travel | Reports to CEO
ABOUT US
Parasol Alliance is an award-winning Managed Services Provider (MSP) exclusively serving the senior living industry. We've earned 24 Best & Brightest workplace awards and have been named a Top 501 MSP in the world by Channel Futures — five years running. We move fast, think big, and genuinely care about our people and the communities we serve.
THE OPPORTUNITY
This is a rare chance to shape the technology future of an entire industry. As VP of Innovation & AI, you'll own how Parasol Alliance thinks about, adopts, and scales artificial intelligence and emerging technology — both inside our organization and across our entire client portfolio.
This isn't a support role or an advisory title. You'll be the architect of our AI and innovation strategy, driving it from vision to execution and making sure every client we serve benefits from what we build. If you're energized by turning ambitious ideas into real outcomes — and by doing it at a company that's genuinely mission-driven — this role was made for you.
WHAT YOU'LL DO
AI Strategy & Internal Adoption
- Own and drive Parasol Alliance's enterprise AI strategy — defining where we go, how fast, and what we build
- Identify, prioritize, and implement AI use cases that improve how we deliver services, operate, and grow
- Establish AI governance frameworks covering responsible use, privacy, security, and risk
- Build internal AI fluency through training, enablement, and hands-on adoption programs for our team
- Serve as Parasol Alliance's internal authority on the evolving AI landscape — keeping us ahead, not catching up
Technology Assessment & Senior Living Innovation
- Lead technology assessments that go beyond infrastructure — uncovering strategic gaps, innovation opportunities, and AI readiness across client organizations
- Monitor and evaluate emerging technologies across senior living, healthcare, and adjacent industries — from resident experience and clinical operations to analytics
- Lead AI and innovation strategy across the full client portfolio — coordinating initiatives, identifying patterns, and scaling what works
- Create client-facing AI readiness assessments, innovation roadmaps, and forward-looking technology strategies
- Translate market trends and emerging technology into actionable, prioritized intelligence that sharpens our clients' competitive edge and informs multi-year investment decisions
- Develop a consistent, scalable framework for how Parasol Alliance identifies, evaluates, and activates innovation across the client base
- Create frameworks that help clients distinguish meaningful innovation from technology that isn't ready for real-world deployment
Organizational & Service Leadership
- Collaborate with Client Success, Engineering, Security, and Sales teams to align innovation with service delivery
- Develop scalable AI and innovation service offerings, methodologies, and intellectual property that differentiate Parasol Alliance
- Support business development when AI, innovation, or strategic technology expertise is needed to win or expand relationships
- Contribute to Parasol Alliance thought leadership through industry events, webinars, and content
WHAT YOU BRING
- Significant experience in technology leadership, innovation strategy, AI adoption, or a related senior role
- Demonstrated ability to develop and execute enterprise AI strategies — not just advise on them
- Strong understanding of generative AI, automation, analytics, cybersecurity, and the emerging technology landscape
- Experience with AI governance, responsible AI adoption, or enterprise-scale AI implementation
- Proven ability to translate complex technology trends into clear, prioritized, business-ready recommendations
- Knowledge of senior living or healthcare technology operations is a strong plus
- Excellent executive communication, facilitation, and presentation skills
- Comfortable leading cross-functional initiatives and influencing without direct authority
- Bachelor's degree in IT, Business, Healthcare Administration, or a related field preferred; equivalent experience considered
- Ability and willingness to travel approximately 30% for client engagements, strategic meetings, and industry events
WHY PARASOL ALLIANCE
- Generous paid time off
- Medical, dental & vision coverage
- Remote-first work environment
- 401(k) with company match
- Certification & development support
- Award-winning workplace culture
- Mission-driven work that matters
GEOGRAPHIC ELIGIBILITY NOTICE
We are currently unable to consider applicants residing in California, Colorado, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Vermont, Virginia, Washington, or Washington D.C. We encourage candidates in all other states to apply.
Parasol Alliance is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other characteristic protected by law.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Parasol Alliance, 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 $212,000 based on 11,866 positions with disclosed compensation.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Parasol Alliance AI Hiring
Parasol Alliance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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