Interested in this AI/ML Engineer role at Jonas Software?
Apply Now →About This Role
Job Description:
--------------------
Vice President, Product Strategy \& AI Innovation
SUMMARY
The Vice President, Product Strategy \& AI Innovation is responsible for defining and executing the company's product, market, and innovation strategy. Reporting directly to the President, this executive role serves as the business leader for the company's software portfolio and is accountable for ensuring product investments drive customer value, competitive differentiation, revenue growth, profitability, and long\-term market leadership.
The VP will lead the transformation of the organization from traditional software development practices to a product\-led, specification\-driven, AI\-enabled operating model. This leader will establish the strategic vision for the company's next generation of commercial construction and field service management solutions, including the development of an AI\-first, agentic ERP platform.
The role is accountable for determining what should be built, why it should be built, and how product investments align with corporate objectives. Engineering leadership remains accountable for how solutions are technically designed, developed, and delivered.
TECHNICAL SKILLS
Strategic Thinking
Business Acumen
Market Insight
Product Vision
Financial Analysis
Executive Communication
Change Leadership
Innovation Leadership
Customer Centricity
Organizational Alignment
Decision Quality
Talent Development
Results Orientation
KEY RESPONSIBILITIES
Product Vision \& Corporate Strategy \- Develop and maintain a multi\-year product strategy aligned with corporate growth objectives. \- Define and communicate the long\-term vision for the company's software portfolio and AI\-first platform strategy. \- Identify emerging market opportunities, customer needs, industry trends, and competitive threats. \- Lead strategic planning activities related to product investments, innovation initiatives, and market expansion. \- Serve as a key member of the executive leadership team contributing to overall corporate strategy.
Product Portfolio Leadership \- Own product portfolio prioritization and investment decisions. \- Establish portfolio governance processes to evaluate product opportunities based on strategic fit, customer impact, ROI, and competitive advantage. \- Define product lifecycle strategies including growth, expansion, modernization, and retirement decisions. \- Ensure all major product initiatives have clearly defined business cases, success metrics, and measurable outcomes.
AI \& Innovation Leadership \- Lead the company's AI transformation strategy. \- Define the roadmap for AI\-enabled workflows, autonomous agents, predictive analytics, and intelligent business automation. \- Evaluate emerging technologies, partnerships, and acquisition opportunities that support the company's innovation objectives. \- Establish frameworks for responsible, scalable, and commercially viable AI adoption across products and internal operations.
Product\-Led Operating Model \- Build and institutionalize a specification\-driven product development process. \- Establish standards for customer discovery, product requirements, solution specifications, acceptance criteria, and outcome measurement. \- Create alignment between Product, Engineering, Customer Success, Professional Services, Sales, and Marketing. \- Ensure product decisions are driven by customer outcomes, market opportunities, and business objectives.
Customer \& Market Intelligence \- Maintain direct relationships with strategic customers and industry leaders. \- Lead customer advisory boards, product councils, and market research initiatives. \- Develop deep understanding of commercial construction, HVAC, plumbing, electrical, mechanical, and field service industries. \- Translate market intelligence into strategic product opportunities.
Commercial Leadership \- Partner with Sales and Marketing on go\-to\-market strategy, positioning, packaging, pricing, and competitive differentiation. \- Analyze product profitability, growth opportunities, and investment returns. \- Support strategic customer engagements and major account opportunities. \- Contribute to annual planning, budgeting, and forecasting activities.
Organizational Leadership \- Lead Product Management, Product Operations, Business Analysis, User Experience, and Product Innovation functions. \- Develop high\-performing product leaders capable of translating business strategy into executable product plans. \- Foster a culture of accountability, customer obsession, innovation, and continuous improvement. \- Build cross\-functional alignment across the organization.
KEY PERFORMANCE INDICATORS
\- Annual Recurring Revenue Growth \- Product Gross Margin \- Product Adoption Rates \- Customer Retention and Net Revenue Retention \- Customer Satisfaction and NPS \- Product Portfolio ROI \- Strategic Initiative Success Rate \- AI Product Adoption Metrics \- Time from Concept to Approved Specification \- Innovation Pipeline Value
QUALIFICATIONS
Education \- Bachelor's Degree in Business, Engineering, Computer Science, Technology Management, or related field. \- MBA or equivalent executive business education strongly preferred.
Experience \- 10\+ years of progressive leadership experience in software, SaaS, ERP, or technology businesses. \- Demonstrated success leading product strategy and business transformation initiatives. \- Experience managing product portfolios and investment decisions. \- Strong understanding of software economics, recurring revenue models, and enterprise software markets. \- Experience with AI\-enabled products, automation technologies, and emerging software platforms preferred. \- Experience serving commercial construction, field service, trades, ERP, or operational software markets is highly desirable.
Leadership Competencies \- Strategic Thinking \- Business Acumen \- Market Insight \- Product Vision \- Financial Analysis \- Executive Communication \- Change Leadership \- Innovation Leadership \- Customer Centricity \- Organizational Alignment \- Decision Quality \- Talent Development \- Results Orientation
SUCCESS PROFILE
The successful candidate is a business strategist first, product leader second, and technology innovator third. They possess the ability to connect market opportunities, customer outcomes, financial performance, and technology investments into a unified product strategy that drives sustainable competitive advantage and long\-term enterprise value.
Business Unit:
------------------
Data\-Basics, Inc.Scheduled Weekly Hours:
---------------------------
40Number of Openings Available:
---------------------------------
1Worker Type:
----------------
RegularMore About Jonas Software:
------------------------------
Jonas Software is a leading provider of enterprise management software solutions, serving a wide range of vertical markets including hospitality, healthcare, construction, education, personal care, fitness, leisure, moving and legal services, to name a few. Within these markets, Jonas is comprised of over 65 distinct brands, each a respected leader in its domain.
Jonas’ vision is to be the branded global leader across these verticals and to be recognized by customers and industry stakeholders as the trusted provider of “Software for Life.” We are committed to technology, product innovation, quality, and exceptional customer service.
Jonas Software supports over 60,000 customers in more than 30 countries. We employ over 6,000 skilled professionals, including industry experts and technology specialists. Across our broader network, we support a global workforce of more than 30,000 employees.
Headquartered in Canada, Jonas Software has a global footprint with offices around the world. We’re a 100% owned subsidiary of Constellation Software Inc., based in Toronto, publicly listed on the TSX (CSU.TO), and a member of the S\&P/TSX 60 Index.
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 Jonas Software, 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 $218,750 based on 3,817 positions with disclosed compensation.
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.
Jonas Software AI Hiring
Jonas Software 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 $185,334 across 717 positions. About 14% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.