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About This Role
Company Description
McFadyen Digital is a leading advisor and implementer of digital commerce and marketplace solutions with a growing AI practice. During three decades of ecommerce work, we’ve built hundreds of enterprise\-scale platforms that cumulatively generate tens of billions of dollars of gross merchandise value (GMV). We’re honored to have helped 10% of the Fortune 500 with their digital commerce initiatives. Over 250 global brands like ABB, AB InBev, Albertsons, American Red Cross, American Eagle Outfitters, FedEx, Louis Vuitton, and Schneider Electric have entrusted their digital transformations to McFadyen. We operate global offices across the US, Brazil, and India.
We’re well known for our thought leadership, such as our recent 500\-page “AI Best Practices” book and corresponding platform www.AI\-Best\-Practices.com. McFadyen Digital has been a trusted employer for nearly four decades and believes that people are the most important part of our company. We’re proud of our Great Place to Work certification.
Learn more about us at our website: www.mcfadyen.com
Job Description
Have you proven your ability to generate qualified leads with AI marketing? Are you an innovative digital marketer who enjoys hands\-on execution and experimentation across a variety of digital channels? Have you designed and launched one\-of\-a\-kind guerilla marketing tactics? Do you like testing new AI tools in a fast\-paced environment that lets you launch them across multiple areas of marketing—web content, email automation, AEO/GEO, newsletters, paid media, content promotion, analytics, and more? Have you led teams to deliver outstanding marketing results? Can you stay ahead of the market by leveraging the latest AI tools before they become mainstream?
If you’re detail\-oriented, ambitious, and eager to grow as a marketer leveraging AI, this role is a great fit. We’re seeking a hands\-on marketing guru that can leverage AI with a globally distributed team to drive market expansion.
You will create and execute digital programs that generate sales leads. You will work closely with the CEO, VP of Sales, and other executives. This is an ideal role for a self\-motivated person who has leveraged the latest AI tools (e.g. Claude Code, OpenClaw, agents) and wants to expand usage for large\-scale B2B marketing.
Top 5 Responsibilities
- The top goal and metric is to drive qualified leads to our sales team that convert into clients.
- Design, oversee, and optimize lead\-generating multi\-channel campaigns \& sequences across email, website content, social channels, webinars, landing pages, ads/SEM, SEO/AEO/GEO, newsletters, retargeting, in\-person events, partner channels, PR, and evolving channels.
- Leverage AI, agents, other tools to automate campaigns and the creation of thought leadership content, web content, video, audio, image and other assets. Be hands\-on enough to guide team members.
- Collaborate with our sales teams to help drive business development with innovative GTM ideas, new tools, AI usage, insights, campaigns, content creation, and analytics/insights.
- As a member of our executive leadership team, help define company strategy, market offerings, and ultimately client success.
*Additional Responsibilities*
- Enhance and maintain our Salesforce CRM and enrich data with tools like ZoomInfo, Clay, LeadMagic, or similar. Help ensure sales contact and pipeline information is accurate.
- Track, report, and constantly improve key metrics with analytics / insights that will drive sales.
- Upgrade, manage, and continuously enhance our mcfadyen.com site and related websites.
- Optimize and report on our AEO/GEO/SEO to improve our domain authority on AI for ecommerce, marketplace, and related topics.
- Lead, mentor, and drive the performance of a handful of direct report marketing team members spread across the US, Brazil, and India.
- Occasionally assist with sales enablement by providing specific content and tools.
- Ensure the marketing team maintains a human\-in\-the\-loop reviewer of all AI\-generated content, email drafts, reports, and other automated workflows. Prevent AI slop.
- Research new tools, channels, and techniques; actively propose innovation with small tests.
- Document and ensure your team follows structured processes for campaigns, content, audiences, templates, and reporting.
- Ensure consistent brand messaging and promotion.
Qualifications
*Top 5 Qualifications*
- 1\-2\+ years of hands\-on use of AI for marketing, content creation, research or other marketing purposes with proven results. Experience building marketing agents that have driven tangible results.
- 10\+ years of experience in digital marketing or marketing related roles.
- Hands\-on experience with leading marketing automation tools, enterprise CRM platforms, customer data platforms (CDP), multi\-touch attribution, analytics and/or other advanced martech tools.
- Strong understanding of digital channels (e.g., Google Ads, LinkedIn Ads, social media, landing pages, email campaigns, SEO/AEO/GEO) and how to optimize engagement and metrics across each channel.
- Ability to be both hands\-on and to manage a team that delivers results.
*Other Preferred Qualifications*
- A self\-driven pioneer that tries the latest technology before it becomes mainstream.
- Experience managing digital initiatives and working with global software development teams.
- Thrives in a fast\-paced environment.
- Comfortable editing simple web pages or landing pages.
- Experience with eCommerce and marketplace technology.
- Experience supporting webinars, virtual events, and physical events.
- Excellent US English communication skills (mandatory).
- Ability to occasionally travel to events (perhaps quarterly) in the USA.
*Top 5 Performance Metrics*
- Number of marketing\-sourced MQLs that are passed to our sales team.
- Marketing\-source MQL to SQL conversion rate (as determined by sales team).
- Volume of high\-value thought\-leadership content published and driving prospect engagement.
- Dollar value of marketing\-sourced bookings.
- A collection of metrics including web traffic, AEO/SEO rankings, LinkedIn traffic, newsletter subscribers, etc.
Our Core Values: Caring, Adaptable, Proactive, Empowering Clients
Keywords:
eCommerce, Marketplace \& Content Platforms: Adobe Commerce (Magento), Mirakl, ChannelAdvisor, BigCommerce, Shopify Plus, commercetools, Oracle Commerce, Elastic Path, Akeneo, Stibo
Technology \& Tools:CRM (Salesforce, HubSpot, Go High Level, PipeDrive), Marketing Automation (Pardot, Marketo, Eloqua, MailChimp, ActiveCampaign) Data (ZoomInfo, Lead Magic, Clay), SaaS Sales, PIM (Product Information Management), CPQ (Configure Price Quote), CMS (Content Management Systems), OMS (Order Management Systems), ERP (Enterprise Resource Planning), BI Tools (Business Intelligence), Microsoft Project, Jira, Linear
AI Tools: Anthropic Claude Code, OpenAI ChatGPT, Google Gemini, Perplexity AI, Microsoft Copilot, xAI Grok, Jasper, Notion AI, Zapier, AWS Bedrock, OpenClaw, TensorFlow, MidJourney, ElevenLabs, Synthesia, Adobe Firefly, Lindy, N8N, MCP Agentic Commerce
Agency / Consultancy Experience: Accenture, Deloitte, Publicis Sapient, PwC, EY, KPMG, Infosys, Cognizant, TCS, Wipro, IBM, HCL, KPS, CGI, DMI, EPAM, BORN, Gorilla, LiveArea, OSF Digital, Omnicom, WPP, DCX, Atos, ThoughtWorks, dentsu, Merkle, AKQA, Atos, Astound, MindCurv, MindTree, Perficient, Acumen Solutions, Bounteous, MRM, Valtech, Mastek, Tech Mahindra, Wunderman Thompson, Capgemini, McKinsey, BCG, Kearney, Roland Berger, or Bain.
McFadyen’s goal is to ensure that our clients are able to maximize the return on their technology investment by providing better service to their customers, partners and internal teams. At McFadyen we truly believe our employees are our most valuable asset. Across our locations in the US, Brazil, and India, we offer a world class work culture that enables top notch delivery for some of the world’s most influential companies.
McFadyen Digital is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity / expression, national origin, disability, protected veteran status, or any other characteristic protected under federal, state, or local law, where applicable.
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 MCFADYEN DIGITAL, 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.
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.
MCFADYEN DIGITAL AI Hiring
MCFADYEN DIGITAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Vienna, VA, US.
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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