Interested in this AI/ML Engineer role at Patrick Industries?
Apply Now →Skills & Technologies
About This Role
Vice President, Artificial Intelligence \& Data
===================================================
Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team of dedicated Team Members who are passionate about delivering high\-quality products and exceptional customer service. As a leading solutions provider serving a diverse range of markets across the United States, our commitment to innovation, quality, and sustainability has positioned us as a high growth, diversified and empowered Team of more than 10,000! Your adventure awaits!
Patrick Industries is building its enterprise AI and data capability from the ground up — and is searching for the executive to lead it. This is a rare “zero\-to\-one” mandate inside a profitable, acquisitive company with 65\+ years of entrepreneurial execution and 85\+ operating brands: a staged, multi\-year investment behind a use\-case portfolio carrying more than $150M of identified value across 70\+ initiatives, spanning customer\-centric operations, aftermarket commerce, and back\-office automation. The Vice President of AI \& Data will set the operating model, formulate the AI and data investment strategy, build and scale the delivery team, own the data foundation on which it all depends, and run the engine that turns strategy into production\-grade and measurable value.
The Role
Reporting to the Chief Information Officer, the Vice President of AI \& Data governs, prioritizes, and delivers the enterprise AI, data, and automation initiatives that drive measurable business value across Patrick Industries. The role is the execution engine behind the enterprise AI strategy — and the steward of the data foundation beneath it — translating prioritized use cases into scalable, production\-grade solutions through a DevOps\-enabled, agile delivery model, and ensuring a disciplined delivery capability that is fast without being fragile.
Operating at the intersection of business and technology, the VP carries full lifecycle accountability — from intake and prioritization through build, deployment, and scaled adoption — and is expected to stay at the leading edge of a fast\-moving field, continuously evaluating new models, agentic frameworks, and tools and translating them into pragmatic, well\-governed advantage. The leader drives clear traceability from each use case to defined KPIs and business outcomes, strengthens the data\-governance leg of the enterprise Digital Backbone, and aligns delivery to Patrick’s IT Strategic Pillars:
- Innovative Advantage – Scale AI\-, data\-, and automation\-driven capabilities that unlock new business value.
- Value Optimization – Ensure measurable ROI, efficiency gains, and capital discipline.
- Agility \& Efficiency – Enable rapid, iterative delivery through modern DevOps practices.
- Resilient Operations – Keep AI and data solutions secure, stable, and well\-governed.
Areas of Responsibility
The mandate spans the operating capabilities the VP will stand up to govern, deliver, and sustain AI and data at enterprise scale.
Govern \& Direct — set the agenda, control the rules, steer the portfolio
- AI \& Data Strategy \& Investment — Own the enterprise AI and data strategy and roadmap, the multi\-year investment plan and budget allocation, the operating model and decision rights, and an outcome thesis tied to defined value levers.
- Data Governance, Policy, Standards \& Risk — Own data governance — ownership and stewardship, quality, master data management, access, and lineage — alongside acceptable\-use policy, an approved\-tool catalog with exception workflow, security/model/vendor risk, and a controls library and risk register.
- Portfolio \& Program Management — Prioritize, sequence, and stage\-gate the portfolio; control scope, budget, and resources; manage cadence, milestones, and dependencies; and track value realization and benefits.
- Training, Change \& Adoption — Build AI and data literacy from the executive team to the frontline, role\-based training paths, change and communications plans, and a champion network that drives durable adoption.
Deliver \& Run — build, run, and sustain the capabilities that produce value
- Enterprise Data Platform \& Architecture — Own the data foundation AI depends on — the lakehouse/fabric bridging 40\+ ERPs, the semantic layer, master data management and entity matching, cataloging, and observability — and sequence AI delivery behind data readiness.
- Product Ownership: LLM Platform \& Utilities — Own the roadmap for shared LLMs, agents, APIs, and utilities, with monitoring, observability, evaluation, and quality controls, plus utilization analytics, financials, and vendor management.
- Product Ownership: AI Solutions — Ensure every production solution has a named owner, a managed backlog and release plan, KPI ownership and user\-feedback loops, and disciplined reuse, consolidation, and sunset decisions.
- Technical Ownership — Set reference architecture, integration patterns, and standards; run SDLC, DevOps, and CI/CD for AI workloads; manage environments, infrastructure\-as\-code, and reliability (SRE); and own production support and incident response.
- Knowledge \& Content Management — Own curated knowledge bases and sources of truth, content lifecycle and access controls, retrieval infrastructure, and data\-quality stewardship with ongoing SME\-driven curation.
Building the Team \& Delivery Engine
A central part of the mandate is to build the people and platform that make delivery repeatable. The VP will recruit and scale a dedicated team from a small founding core to roughly twenty professionals over three years — solution architecture, AI/ML and software engineering, data engineering and architecture, DevOps/MLOps, product management, and data and solution governance — operating a lean internal model that orchestrates strategic delivery partners and brand adoption rather than depending on them. The team stands up the reusable data platform, pipelines, and engineering playbooks that bend the cost curve so each successive use case is faster and cheaper than the last, while Patrick retains the architecture, intellectual property, and institutional knowledge.
Staying at the frontier of AI and data
- Maintain an active scan of frontier models, agentic frameworks, and tooling with a disciplined evaluation pipeline that separates durable capability from hype, keeping the approved\-tool catalog and reference patterns current without compromising security or governance.
- Translate emerging capability into pragmatic roadmap and investment decisions, and continuously upskill the team so Patrick’s practice compounds rather than ages.
Traceability to the IT Strategy
Every responsibility traces to Patrick’s IT Strategic Pillars and the enterprise Digital Backbone (Architecture \| Data Governance \| Talent) across the Stabilize Accelerate Differentiate journey — and, through them, to profitable growth, operational discipline, capital stewardship, and teams built for today and tomorrow.
Strategic Pillar
How this role advances it
Innovative Advantage
Scales AI, data, and automation that expand margin, insight, and competitive differentiation, unlocking new growth across customer, aftermarket, and operations.
Value Optimization
Formulates and governs the AI and data investment for measurable ROI; enforces portfolio discipline, benefits tracking, and total\-cost\-of\-ownership control.
Agility \& Efficiency
Operates a product\-centric, DevOps\-enabled delivery model with a predictable cadence and rapid time\-to\-value.
Resilient Operations
Keeps AI and data solutions secure, reliable, and well\-governed through standards, controls, SRE, and incident response.
Candidate Profile
- Proven executive leadership in AI, data, automation, advanced analytics, or digital product delivery, with a track record of taking solutions from pilot to enterprise scale.
- Strategic command of AI and data investment — able to shape a multi\-year roadmap and budget, prioritize for ROI, and make disciplined build / buy / partner decisions.
- Deep experience with modern data platforms and governance (lakehouse/fabric, MDM, cataloging, data quality and lineage) and the modern AI stack (LLMs and agentic systems, RAG, MLOps/LLMOps, cloud) — with the habit of staying at the frontier.
- Strong experience operating DevOps and agile delivery at enterprise scale, with a disciplined, metrics\-driven delivery capability.
- Experience leading within federated or decentralized business environments and influencing senior business stakeholders.
- Deep understanding of enterprise governance disciplines — security, data, architecture, and compliance — and executive communication skills suited to C\-suite and Board engagement.
- A builder who thrives in a relatively undefined, zero\-to\-one environment and is energized by standing up a team, a platform, and an operating model.
Leadership Competencies:
Executing for Results
- Sets clear and challenging goals while committing the organization to improved performance; tenacious and accountable in driving results.
- Comfortable with ambiguity; adapts nimbly and leads others through complex situations, taking smart, well\-considered risks.
- Viewed as having high integrity and forethought; acts transparently and consistently, always considering what is best for the organization.
Leadership
- Leads by example, demonstrating Patrick’s principles of effective leadership: Leading for Positive Influence and culture, Leading with Humility, Embracing Responsibility, Communicating with Excellence, Leading with Accurate and Social Awareness, Building Healthy Accountability, and Servant Leadership.
- A diplomat who promotes healthy debate toward “win\-win” outcomes and inspires teams with an approachable style.
- Thrives in a relatively undefined environment, unafraid to “roll up sleeves” across a wide range of topics, projects, and deliverables.
- Self\-reflective and open to feedback; empowers individuals and teams and drives continuous improvement.
Relationships \& Influence
- Builds strong relationships with stakeholders through emotional intelligence and clear, persuasive communication; inspires trust and followership.
- Brings notable business understanding and developed relationships across industries and technologies.
At Patrick Industries, BETTER Together is our commitment to being our best while striving to bring out the best in one another as we join forces Individually, as Teams, with our Business Units, with our Customers, our Communities and within our entire Patrick family.
Patrick is an Equal Opportunity Employer.
Location:
Elkhart, Indiana, US, 46516
Work Arrangement: on\-site
Business Unit: Patrick Industries Inc Corp
Nearest Major Market: Elkhart
Nearest Secondary Market: South Bend
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Patrick Industries, 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 $181,170 based on 12,692 positions with disclosed compensation.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Patrick Industries AI Hiring
Patrick Industries has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Elkhart, IN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.