Interested in this AI/ML Engineer role at World Wide Technology?
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Qualifications:
- Minimum 12 years of enterprise technology sales experience, including demonstrated success in quota\-carrying roles with consistent attainment or overachievement.
- Minimum 3 years of sales management or team leadership experience, with direct accountability for the performance of quota\-carrying sales professionals.
- Proven track record of building and developing high\-performing sales teams, with specific experience coaching pipeline generation, consultative selling, and co\-selling behaviors.
- Demonstrated ability to run a structured sales operating cadence: pipeline reviews, forecast calls, deal\-stage governance, and performance management rhythms that drive consistent execution.
- Sufficient AI domain fluency to coach POD members credibly, engage confidently in customer conversations, and make informed decisions about resource deployment into specific opportunities. AI literacy is non\-negotiable.
- Experience managing within a co\-selling or specialist sales model, partnering effectively with field account teams who own the primary customer relationship.
- Demonstrated ability to engage personally and credibly at the IT and LOB executive level, including executive customer conversations and high\-stakes proposal presentations.
- Strong interpersonal and organizational leadership skills, with the ability to influence and align cross\-functional resources — shared specialists, OEM partners, and field sales leaders — who do not report directly into the role.
- Bachelor's degree required.
- Preferred: Prior experience managing AI, cloud, data, or advanced infrastructure\-focused sales teams at a solutions provider, consultancy, or enterprise OEM.
- Preferred: Existing relationships with AI ecosystem partners — NVIDIA, hyperscale cloud providers, AI infrastructure OEMs, and emerging AI software vendors.
- Preferred: Familiarity with WWT's account base, Advanced Technology Center model, or lab\-led selling methodologies.
- Preferred: Experience building or scaling specialist sales team models within large enterprise sales organizations.
- Preferred: MBA or advanced degree in business, technology, or a related discipline.
Want to learn more about Enterprise AI Strategy \& GTM Execution? Check us out on our platform:
https://www.wwt.com/all\-categories/artificial\-intelligence
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $185,00 \- $225,000 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay.
The well\-being of WWT employees is essential. So, when it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full\-time employees:
- Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
- Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement
- Paid Time Off: PTO and Sick Leave (starting at 20 days per year) \& Holidays (10 per year), Parental Leave, Military Leave, Bereavement
- Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for All!
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for All!
If you have any questions or concerns about this posting, please email [email protected].
\#LI\-MG2
Requirements:
Why WWT?
At World Wide Technology, we work together to make a new world happen. Our important work benefits our clients and partners as much as it does our people and communities across the globe. WWT is dedicated to achieving its mission of creating a profitable growth company that is also a Great Place to Work for All. We achieve this through our world\-class culture, generous benefits and by delivering cutting\-edge technology solutions for our clients.
Founded in 1990, WWT is a global technology solutions provider leading the AI and Digital Revolution. WWT combines the power of strategy, execution and partnership to accelerate digital transformational outcomes for organizations around the globe. Through its Advanced Technology Center, a collaborative ecosystem of the world's most advanced hardware and software solutions, WWT helps clients and partners conceptualize, test and validate innovative technology solutions for the best business outcomes and then deploys them at scale through its global warehousing, distribution and integration capabilities.
With over 12,000 employees across WWT and Softchoice and more than 60 locations around the world, WWT's culture, built on a set of core values and established leadership philosophies, has been recognized 15 years in a row by Fortune and Great Place to Work® for its unique blend of determination, innovation and creating a great place to work for all.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
What is the Enterprise AI Strategy \& GTM Execution team?
WWT's Enterprise AI Strategy \& GTM Execution team is at the center of WWT's AI transformation, responsible for leading the company's go\-to\-market motion across the full AI solution stack — from infrastructure and platforms to consulting, advisory, and managed services. As part of this team, the AI Solutions Director plays a critical leadership role, owning the performance of 2–3 AI POD teams and serving as the primary bridge between WWT's enterprise AI capabilities and the customers who need them most. This is a rare opportunity to lead a purpose\-built AI sales organization at one of the world's leading technology solutions providers, with the backing of WWT's Advanced Technology Center, world\-class OEM partnerships, and a culture built on collaboration, accountability, and innovation.
What will you be doing?
The AI Solutions Director is WWT's frontline AI leadership role, responsible for guiding and managing 2–3 AI POD teams within an assigned business unit. Reporting directly to the SVP of Enterprise AI Strategy \& GTM Execution, this is a player\-coach position that balances team leadership with direct personal engagement in the most complex, high\-value customer pursuits. Each AI POD is composed of three dedicated roles — an AI Solutions Executive, an AI Solutions Architect, and an AI Services Consultant — working in concert to generate pipeline, win opportunities, and drive AI adoption across WWT's enterprise account base. The AI Solutions Director owns overall POD performance including pipeline health, forecast accuracy, margin attainment, and revenue growth across all assigned PODs.
Responsibilities:
- Lead and manage 2–3 AI PODs within the assigned business unit with direct accountability for team performance, including pipeline growth, win rates, revenue attainment, and margin targets.
- Set clear performance expectations for AI Solutions Executives, Solutions Architects, and Services Consultants; provide ongoing coaching, feedback, and development support to build a high\-performing team.
- Run a structured weekly AI GTM operating cadence across all PODs: pipeline reviews, deal stage progression, resource allocation decisions, and forecast submissions.
- Maintain rigorous pipeline governance standards: accurate CRM hygiene, realistic stage\-gating, clear next\-step accountability, and consistent documentation of deal health and risk factors.
- Engage directly and personally in the most strategic customer pursuits within the assigned business unit, leading IT and line\-of\-business executive conversations for complex, multi\-stakeholder engagements.
- Lead or co\-lead executive briefings, AI strategy sessions, and high\-stakes proposal presentations where customer relationships or deal sizes require Director\-level involvement.
- Coordinate the deployment of shared resources — Industry Advisors, AI Development Specialists, Executive Advisors, and SC\&E teams — into POD\-level pursuits based on account complexity, vertical alignment, and deal stage.
- Partner with OEM\-embedded resources from NVIDIA, Dell, Cisco, HPE, and AI Native Engineering partners on BU\-level joint pipeline planning, co\-sell execution, and partner program utilization.
- Work closely with Regional Sales, Engineering, and Services leadership to ensure AI POD resources are fully integrated into regional account planning, territory strategy, and field sales team activities.
- Build strong, trust\-based working relationships with Regional Vice Presidents and senior field managers, establishing the AI POD as a valued resource that field teams actively leverage in opportunities.
- Foster a collaborative, accountable, and high\-energy POD culture where team members support one another, share intelligence, and consistently execute with discipline.
- Partner with HR and talent acquisition to support hiring decisions for POD roles, ensuring new team members meet the skill and culture standards required to contribute quickly.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At World Wide Technology, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
World Wide Technology AI Hiring
World Wide Technology has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, Hartford, CT, US, St. Louis, MO, US. Compensation range: $104K - $300K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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