Interested in this AI/ML Engineer role at Ferguson?
Apply Now →Skills & Technologies
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 COE Sr. Manager
----------------------
The AI COE Senior Manager serves as the architect of Ferguson's AI Center of Excellence, designing the governance infrastructure, operating frameworks, and coordination mechanisms that enable responsible AI adoption at enterprise scale. This role establishes the key organizational systems, from AI Council operations to intake processes to risk management frameworks, that support enterprise AI enablement.
Reporting to the VP of AI, this position operates at the intersection of governance, operations, and strategy - leading the cross-functional AI Council and GRC sub-committee, establishing intake and prioritization processes, developing Responsible AI Use Guidelines in partnership with Legal and Information Security, and managing strategic vendor relationships. Success requires balancing competing priorities: enabling innovation velocity while managing risk, establishing governance rigor without creating bureaucracy, and coordinating across functions with different risk appetites. The ideal candidate thinks in scalable systems and reusable patterns. They build trust with business executives who want to deploy AI and with compliance team members focused on safety. They see governance as enabling responsible transformation rather than blocking progress.
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:
-------------------------
AI Governance & Council Operations
- Design and facilitate AI Council meetings, ensuring productive dialogue between functional leaders and executive business customers
- Develop and maintain AI Enablement operational framework including project intake, prioritization criteria, and decision-making processes
- Track and report on portfolio-wide AI initiatives, identifying dependencies, risks, and resource conflicts
- Create transparency mechanisms (dashboards, status reports, customer updates) that build confidence in AI program execution
- Manage escalations and remove organizational blockers that impede AI adoption
Program Infrastructure & Standards
- Establish program management standards, templates, and operating rhythms that scale across the AI Enablement organization
- Manage budget tracking and resource allocation across initiatives
- Build case frameworks that quantify AI value in terms meaningful to Ferguson's leadership
- Standardize playbooks and templates for AI initiative charters, risk assessments, and value tracking
- Create knowledge management systems that capture lessons and standard processes
- Coordinate vendor relationships and contract management as the AI program scales
Agent Registry & Quality Management
- Design and maintain agent registry tracking all AI agents (owner, purpose, data sources, risk level, approval status, performance)
- Establish agent lifecycle management from ideation through approval, deployment, monitoring, retirement
- Create agent review workflows that apply appropriate rigor based on risk level
- Establish agent performance monitoring standards through usage analytics, error rates, user feedback, business impact
- Coordinate agent quality reviews and retirement decisions with partners
- Ensure consistency across agent portfolio, ensuring a best-in-class user experience and no contradictory or confusing experiences
Vendor & Partnership Management
- Own relationships with enterprise AI platform vendors (Microsoft/Google/Databricks) including critical issues, roadmap influence, utilization
- Evaluate and onboard additional enterprise standard AI tools and services as organizational needs evolve
- Assess build-vs-buy-vs-partner decisions for AI capabilities based on strategic value and organizational capacity
- Maintain relationships with AI-focused professional services firms or implementation partners
AI Governance, Risk & Compliance
- Lead cross-functional AI Governance, Risk & Compliance committee including representatives from Legal, Information Security, Privacy, Risk Management, Compliance, and HR
- Develop and maintain AI governance policies in partnership with Legal, Information Security, Privacy, and Risk Management
- Translate enterprise policies (data security, privacy, acceptable use, ethical standards) into practical AI-specific guidance
- Establish proactive monitoring processes for AI safety issues and develop escalation and incidence response protocols
- Monitor for emerging risks: shadow AI usage, unapproved tools, policy circumvention
- Track regulatory landscape and prepare Ferguson for compliance with emerging AI regulations
Required Qualifications:
----------------------------
Experience & Background
- 10+ years designing enterprise governance frameworks in complex organizations, ideally in technology transformation or AI/ML adoption contexts
- Proven track record establishing operating models and decision frameworks that balanced innovation velocity with risk management
- Experience leading cross-functional councils and partnering with Legal, InfoSec, Privacy, and Compliance to operationalize regulatory requirements
- Understanding of AI/ML technologies, capabilities, and governance considerations; direct AI adoption experience preferred
Strategic Design & Systems Thinking
- Exceptional systems designer who thinks in scalable frameworks and distributed accountability rather than one-off solutions
- Translates complex requirements into clear, actionable policies; designs "just enough" governance that enables rather than constrains
- Creates frameworks that work at pilot stage and remain effective at enterprise scale
Leadership & Stakeholder Management
- Executive presence with ability to build consensus across functions with competing priorities
- Exceptional facilitation skills; earns trust from both business leaders and compliance stakeholders
- High integrity; navigates organizational politics effectively in cultures that emphasize collaboration
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.*
$11,633.34 - $18,616.67
*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 $139K-$223K range is above 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. Senior-level AI roles across all categories have a median of $225,000. This role's midpoint ($181K) sits 18% above the category median. Disclosed range: $139K to $223K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.