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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.
IT Manager, AI Platform Engineering \& Data Science
The Manager, Platform Engineering \& Data Science is a key technology leader within Ferguson's AI Technology and Innovation organization, responsible for building and evolving the platforms, engineering standards, and cloud capabilities that enable scalable AI, machine learning, and software delivery across the enterprise. This role leads a team of platform engineers, architects, and technical specialists who provide the foundational capabilities that power AI innovation, modern application development, and data science initiatives. Working across a hybrid\-cloud environment, with Google Cloud Platform (GCP) as the primary hyperscaler and Azure supporting hybrid workloads and legacy integrations, this leader will drive platform strategy, cloud architecture, developer experience, DevSecOps maturity, and MLOps capabilities that accelerate the delivery of secure, reliable, and scalable solutions.
Reporting to the Director, AI Technology and Innovation, the Manager, Platform Engineering \& Data Science will collaborate closely with experts in AI Engineering, Data Science, Product, Architecture, and IT to modernize technology platforms, improve engineering efficiency, and enable teams to move rapidly from experimentation to production while delivering measurable business value.
Location: This role is approved to be remote within the United States; however, candidates must reside in the Eastern or Central time zones to support collaboration with team members based in Newport News, VA, and our Global Capability Center (GCC) team in India. Associates located near Newport News may work in a hybrid arrangement in accordance with company policy. Occasional travel to Ferguson headquarters in Newport News, VA, and other company locations, including Tampa, FL, is expected.Duties and Responsibilities:
- Lead, coach, and develop a high\-performing team of platform engineers, architects, and technical specialists.
- Define and execute the platform engineering, cloud, and data science roadmap in support of Ferguson's AI and technology strategy.
- Design, build, and optimize scalable, secure, and reliable platform capabilities across Google Cloud Platform (GCP) and hybrid\-cloud environments.
- Partner with AI Engineering, Data Science, Product, and Architecture teams to enable the development, deployment, and operation of AI and machine learning solutions.
- Drive DevSecOps, CI/CD, infrastructure automation, and platform reliability standards to improve delivery speed, quality, and operational efficiency.
- Own and enhance MLOps and data science platform capabilities, supporting model development, training, deployment, and monitoring at scale.
- Establish engineering standards, architecture patterns, API strategies, and reusable platform services that accelerate software delivery.
- Lead platform modernization initiatives, including cloud adoption, developer experience improvements, automation, and legacy technology retirement.
- Manage project delivery, budgets, vendor relationships, and team capacity to ensure successful execution of critical initiatives.
- Supervise platform performance, security, availability, and scalability while proactively identifying and mitigating risks.
- Build strong partnerships with business and technology leaders to align platform investments with organizational priorities.
- Stay ahead of emerging cloud, AI, data, and engineering technologies to drive innovation and continuous improvement.
Qualifications and Requirements:
- Bachelor’s degree in information technology, computer science or related field preferred, or equivalent experience.
- Must have at least 5 years of hands\-on experience in platform engineering. Experience in machine learning implementation and data analytics enablement is required. You should have broad knowledge of how platform capabilities support integration and innovation across different business domains.
- Must have prior experience directly leading engineering or technical talent, including performance management and career development for direct reports; experience with offshore/onsite consultants preferred.
- Must have direct experience in software programming with Java and/or Python, along with Software Architecture and Engineering expertise.
- Must have experience in secure software delivery (DevSecOps) and continuous integration/continuous deployment (CI/CD), and pipeline development.
- Must have hands\-on experience architecting and operating platform services on Google Cloud Platform (GCP) as the primary hyperscaler, with working knowledge of Azure to support hybrid\-cloud workloads and legacy system integration.
- Experience enabling data science and ML workflows using cloud\-native tooling (e.g., Vertex AI, BigQuery, or equivalent feature\-store, pipeline\-orchestration, and model\-serving constructs) strongly preferred.
- Must have experience creating operational dashboards, telemetry configurations and alerting templates for end\-to\-end flow of data services using APM and Data Logging solutions such as AppDynamics, DataDog etc.
- Solid understanding and experience implementing software design patterns and modern standards.
- Must be a hands\-on software engineer able to work alongside a cross\-functional team of software engineers, software architects, data scientists, and software quality engineers as needed.
- Demonstrates high aptitude, initiative, and self\-drive — proactively finds opportunities to improve the platform and data science tooling rather than waiting for direction, and leads technical work hands\-on rather than purely delegating.
- Strong leadership and interpersonal relationship building skills.
- Strong written communication skills with the ability to deliver compelling presentations.
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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$7,568\.91 \- $13,247\.76*
*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 $7K-$13K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($10K) sits 95% below the category median. Disclosed range: $7K to $13K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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: $13K - $13K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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