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About This Role
Job Posting:
Since 1953, Ferguson has been a source of quality supplies for a variety of industries. Together We Build Better infrastructure, better homes and better businesses. We exist to make our customers’ complex projects simple, successful, and sustainable. We proactively solve problems, adapt and grow to continuously serve our customers, communities and each other. Ferguson, a Fortune 500 company, is proud to provide best-in-class products, service and capabilities across the following industries: Commercial/Mechanical, Facilities Supply, Fire and Fabrication, HVAC, Industrial, Residential Trade, Residential Building and Remodel, Waterworks and Residential Digital Commerce. Ferguson has approximately 36,000 associates across 1,700 locations. Ferguson is a community of proud associates who operate with the shared purpose of building something meaningful. You will build a career that you are proud of, at a company you can believe in.
AI Adoption & Enablement Manager
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The AI Adoption & Enablement Manager is the human architect of Ferguson's AI transformation, responsible for ensuring that technology adoption translates into genuine capability building, behavioral change, and cultural evolution. This role sits at the critical intersection of Ferguson's human-first AI philosophy and its execution, designing and delivering the experiences that help associates see AI as amplifying their expertise.
Working with the VP of AI Enablement, this position owns the fluency framework that builds AI literacy and capability across Ferguson's diverse associate population, from counter representatives to sales associates to HQ functions. Equally important, this person works hands-on with functional teams to discover use cases, embed AI into real workflows, and coach teams through early adoption. Success requires deep empathy for how people experience change. It also requires a sophisticated understanding of adult learning principles. Candidates need enough technical proficiency to guide practical implementation. They must have political savvy to navigate a culture where respect, trust, and credibility are earned through demonstrated care for people's success.
Location: This role is approved to be either Remote within the United States or Hybrid for associates in Newport News, VA, in accordance with company policy.
Key Responsibilities:
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Fluency Framework Design & Delivery
- Design comprehensive AI fluency curriculum spanning awareness, literacy, capability, and mastery levels tailored to Ferguson's role segments
- Partner with the Ferguson Academy team to develop engaging, practical learning experiences that connect AI capabilities directly to associates' daily challenges
- Build role-specific use case libraries that demonstrate how AI enhances rather than replaces human expertise
- Pilot, iterate, and scale training programs based on learner feedback and business impact data
- Deliver high-impact training sessions that leave associates energized and confident
Functional Adoption & Workflow Integration (30%)
- Lead functional group workshops tailored to specific team workflows, challenges, and success metrics (Sales, Operations, Product, Branches, etc.)
- Facilitate use case discovery sessions that translate AI capabilities into practical applications for each function's actual work
- Partner hands-on with functional teams to embed AI into existing processes, systems, and operating rhythms - not as theoretical add-ons but as natural workflow enhancements
- Serve as coach and on-the-ground advisor during early adoption phases, troubleshooting barriers and building confidence through direct support
- Develop function-specific playbooks, templates, and enablement materials based on actual implementation experience and observed patterns
- Build deep relationships with functional leaders and frontline teams that provide ongoing insight into adoption health and emerging needs
- Collaborate with AI Platform Engineer(s) to ensure functional requirements and workflow realities inform platform configuration and agent development
Change Management & Adoption Strategy
- Develop and execute change management strategy grounded in emotion-based approaches appropriate for Ferguson's relationship-driven culture
- Identify, recruit, and enable AI champions across functions and geographies - particularly respected veterans whose endorsement influences broader adoption
- Design interventions that address specific adoption barriers (fear, skepticism, inertia, technical confidence) at individual and organizational levels
- Create feedback mechanisms that surface resistance early, allowing for proactive intervention before attitudes harden
- Partner with functional leaders to embed AI adoption into existing team rhythms, performance conversations, and recognition programs
- Work with AI COE Manager to ensure adoption insights inform governance decisions and resource allocation
Success Story Development & Internal Marketing
- Build systematic approach to identifying, documenting, and amplifying early wins and success stories from functional implementations
- Develop compelling narratives that illustrate AI's value in language that resonates with Ferguson's culture (customer impact, associate empowerment, revenue growth)
- Create multi-format success story library (videos, written cases, metrics dashboards, associate testimonials) for different audiences
- Partner with Communications on internal communication strategies that build momentum and maintains visibility for AI initiatives
- Design recognition programs that celebrate both AI champions and associates achieving measurable results with AI
- Turn skeptics-turned-believers into powerful advocates by helping them articulate their transformation journey
Required Qualifications:
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Experience & Background
- 7-10 years in organizational change management, learning & development, or transformation roles, with significant focus on technology adoption and hands-on functional implementation
- Demonstrated success driving behavioral change in large, distributed, relationship-driven organizations (distribution, field sales, manufacturing, healthcare)
- Experience designing and delivering enterprise-wide learning programs that measurably changed how people work
- Must have deep comfort with emerging technology concepts and ability to learn platforms quickly; direct experience with AI/ML technology adoption strongly preferred
- Track record of working with frontline/field populations, not just corporate headquarters or knowledge workers
- Experience in operational excellence, sales enablement, or business transformation roles where you embedded new capabilities into actual workflows (not just trained people on them)
Expertise & Methodologies
- Strong grounding in change management frameworks (Kotter, ADKAR, Bridges) with ability to adapt rather than apply dogmatically
- Expertise in adult learning principles, instructional design, and experiential learning approaches
- Understanding of AI/ML technologies and enterprise AI platforms sufficient to: translate capabilities into practical applications, demo basic functionality, troubleshoot common user issues, and know when to escalate to technical resources
- Skilled in facilitation techniques for use case discovery, design thinking workshops, and process improvement sessions
- Comfortable with workflow analysis and process mapping. Can quickly grasp how functional teams actually work and spot integration opportunities.
Interpersonal & Communication Skills
- Exceptional emotional intelligence and ability to read individual and group dynamics in real-time
- Comfortable spanning from strategy conversations to hands-on coaching in a branch office or warehouse
- Compelling facilitator and storyteller who makes complex concepts accessible and engaging across education and experience levels
- Ability to build trust quickly with diverse team members, particularly those skeptical of change initiatives or corporate programs
- Cultural sensitivity and humility; understands that respect in Ferguson's culture is earned through demonstrated competence and care, not conferred by title
- Active listener who asks good questions and makes people feel heard even when delivering difficult messages
At Ferguson, we care for each other. We value our well-being just as much as our hard work. We are committed to a holistic approach towards benefits plans and programs that support the mental, physical and financial well-being of our associates. Our competitive offering not only includes benefits like health, dental, vision, paid time off, life insurance and a 401(k) with a company match, but our associates also enjoy additional meaningful and inclusive enhancements that are adaptable to their diverse situations and needs, including mental health coverage, gender affirming and family building benefits, paid parental leave, associate discounts, community involvement opportunities and more!
#LI-REMOTE
Pay Range:
*Actual pay rate may vary depending upon location. The estimated pay range for this position is below. The specific rate will depend on a candidate’s qualifications and prior experience.*
$9,700.00 - $15,516.67
*Estimated Ranges displayed are Monthly for Salaried roles* OR *Hourly for all other roles.*
This role is Bonus or Incentive Plan eligible.
Ferguson complies with all wage regulations. The starting wage may be higher in certain locations based on local or state wage requirements.
*The Company is an equal opportunity employer as well as a government contractor that shall abide by the requirements of 41 CFR 60-300.5(a), which prohibits discrimination against qualified protected Veterans and the requirements of 41 CFR 60-741.5(A), which prohibits discrimination against qualified individuals on the basis of disability.*
*Ferguson Enterprises, LLC. is an equal employment employer* *F/M/Disability/Vet/Sexual* *Orientation/Gender* *Identity.*
Equal Employment Opportunity and Reasonable Accommodation Information
Salary Context
This $116K-$186K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 roles with salary data).
View full AI/ML Engineer salary data →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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Ferguson, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. Disclosed range: $116K to $186K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Ferguson AI Hiring
Ferguson has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $186K - $223K.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 7% 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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