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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.
Principal Solutions Architect – Master Data, Product Data & AI Automation
The Principal Solutions Architect will lead the technical architecture for Ferguson’s master data capabilities, with an immediate focus on product data. This role will set architecture direction, guide solution design, and drive scalable, secure, and sustainable capabilities across data quality, AI-enabled classification and automation, enrichment, and cross-reference solutions. The architect will serve as a senior technical leader across product data and adjacent master data domains, translating sophisticated business and technical needs into solutions that improve data quality, operational efficiency, customer experience, and business value.
This is a Principal-level role requiring recognized expertise in Master Data Management, solutions architecture, product data architecture, and practical application of AI and automation. The ideal candidate will bring deep technical depth, enterprise architecture judgment, and the ability to influence business, technology, product, data governance, analytics, and vendor partners across sophisticated initiatives.
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.
Duties and Responsibilities:
- Defines strategic architecture direction for enterprise master data capabilities, with emphasis on product data, data quality, AI automation, enrichment, and cross-reference solutions.
- Translates complex business, data, AI, and technical requirements into scalable solution designs, roadmaps, implementation guidance, and architecture guardrails.
- Leads architecture for product data models, hierarchies, taxonomies, mappings, transformations, validation rules, match and merge logic, and enrichment workflows.
- Shapes responsible AI-enabled patterns that improve data quality, accelerate enrichment, automate repeatable analysis, and support human-in-the-loop review.
- Reviews solution architecture to ensure security, compliance, scalability, performance, reliability, maintainability, and operational support requirements are met.
- Provides technical leadership and mentorship to architects, engineers, analysts, product owners, and delivery teams building or consuming master data capabilities.
- Identifies architecture risks, gaps, dependencies, and trade-offs; recommends practical solutions that balance speed, quality, cost, supportability, and long-term sustainability.
- Partners with senior business and technology leadership, vendors and third-party providers to evaluate capabilities, resolve complex issues, assess roadmaps, and align solutions to Ferguson’s master data and AI strategy.
Qualifications and Requirements:
- 12+ years of IT experience with significant depth in data architecture, solutions architecture, enterprise application architecture, or related disciplines.
- 7+ years of hands-on experience designing, implementing, or governing enterprise Master Data Management solutions.
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Management, or related field; equivalent experience may be considered.
- Deep expertise in master data architecture, including domain modeling, data quality, governance, hierarchy management, stewardship, matching, merging, survivorship, and integration patterns.
- Experience with major MDM platforms such as Stibo Systems, Informatica MDM, Reltio, SAP MDG, Semarchy, or similar platforms.
- Experience with modern cloud data, analytics, and AI platforms, including Databricks Lakehouse, Databricks AI/BI capabilities such as Genie, Claude or similar generative AI tools, Azure Data Factory, Azure Data Lake, cloud storage, SQL and NoSQL platforms, APIs, and modern integration patterns.
- Good experience with product data capabilities, including product information management, enrichment, classification, cross-reference, catalog data, and item setup.
- Practical experience applying AI, machine learning, generative AI, or automation to improve data quality, enrichment, classification, validation, matching, or operational workflows.
- Proven Principal-level ability to define target-state architecture, influence senior leaders, solve complex problems, mentor technical teams, and deliver scalable architecture strategies.
Preferred Experience:
- Experience in wholesale, distribution, eCommerce, or other product-data-intensive industries.
- Experience with product data governance operating models, stewardship workflows, data ownership models, and measurable data quality programs.
- Experience shaping responsible AI patterns, human-in-the-loop review models, AI evaluation approaches, and operational controls for AI-enabled data processes.
- Relevant certifications such as Azure Solutions Architect Expert, Databricks, data architecture, cloud architecture, or similar are a plus.
Why Join Us:
You’ll play a central role in shaping Ferguson’s master data and product data strategy at a time when trusted data, AI-enabled automation, and scalable data quality capabilities are critical to how we serve our customers and run the business. You’ll work across a broad technology landscape in a collaborative environment that values innovation, governance, operational excellence, and measurable business impact, with meaningful opportunities to influence enterprise architecture direction and modernize how Ferguson manages and activates master data.
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
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Pay Range:
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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.
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$10,594.74 - $18,536.46*
Estimated Ranges displayed are Monthly for Salaried roles OR Hourly for all other roles.
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This role is Bonus or Incentive Plan eligible.
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Ferguson complies with all wage regulations. The starting wage may be higher in certain locations based on local or state wage requirements.
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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 $10K-$18K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($14K) sits 93% below the category median. Disclosed range: $10K to $18K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ferguson AI Hiring
Ferguson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $18K - $18K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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