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About This Role
Performance Expectations
- Achievement of assigned AI revenue quota, measured across product, infrastructure, and consulting/advisory services bookings.
- Pipeline generation volume, velocity, and conversion rates against defined regional targets while growing overall account participation rates.
- Services growth aligned to AI engagements, including labs, supply chain, deployment, plus consulting and advisory services that drive multi\-phase account development.
- Field collaboration effectiveness: measured by joint pipeline generated with field account teams, inclusion in account planning, and field team satisfaction/feedback.
- Partner co\-sell activity: earn and secure deal registrations, drive joint pipeline developed with AI partner sales teams, leverage partner programs to improve profitability and win rates.
- Presentation and proposal quality: achieve high win rates on competitive opportunities where AI Solutions Executive led the customer\-facing pitch.
- Operational discipline: CRM accuracy, forecast reliability, and consistent participation in GTM operating cadences.
Required Qualifications
- Minimum 10 years of enterprise technology sales experience, with a demonstrated track record of consistently meeting or exceeding quota targets.
- Proven experience selling both product/infrastructure solutions and consulting/advisory services to enterprise customers.
- Demonstrated ability to generate pipeline proactively—not solely manage inbound opportunities—with a hunter's mentality and a track record of creating demand in complex enterprise accounts.
- Strong working knowledge of the AI landscape: ability to be conversant and credible in discussing AI solutions, platforms, tools, use cases, deployment models, value drivers, and risk considerations. This is a business\-value and consultative\-selling role, not a hands\-on technical practitioner role.
- Proven ability to create and deliver high\-impact, executive\-quality pitch decks and proposals that translate complex technology into clear business narratives.
- Experience collaborating effectively within co\-selling or specialist sales models, partnering with field account teams who are responsible for leading the primary customer relationship.
- Strong interpersonal and relationship\-building skills, with the ability to earn trust and credibility with both internal field teams and external partner organizations.
- Disciplined pipeline management skills: CRM proficiency, accurate forecasting, and commitment to operational reporting requirements.
- Bachelor's degree required.
Preferred Qualifications
- Prior experience selling AI, cloud, data, or advanced infrastructure solutions at a solutions provider, consultancy, or enterprise OEM.
- Existing relationships with AI ecosystem partners (NVIDIA, OEMs, hyperscale cloud providers, AI software vendors).
- Experience with WWT's account base, Advanced Technology Center model, or lab\-led selling methodologies.
- MBA or advanced degree in a business, technology, or analytics discipline.
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 $165,000\.00 to $195,000\.00 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.
Want to learn more about Enterprise AI Strategy \& GTM Execution? Check us out on our platform:
https://www.wwt.com/all\-categories/artificial\-intelligence
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 \& Holidays, Parental Leave, Sick 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!
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. The team will serve as the primary bridge between WWT's enterprise AI capabilities and the customers who need them most. This is a rare opportunity to be a part of 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 Executive is a high\-impact, quota\-carrying sales role dedicated to generating, qualifying, and closing AI pipeline across WWT's existing enterprise account base within an assigned sales region. This role operates as a focused AI co\-selling resource, partnering closely with field sales teams who own the broader customer relationships to identify, develop, and win AI\-specific opportunities.
The AI Solutions Executive must combine deep knowledge of AI solutions, use cases, platforms, and technologies with polished consultative selling skills. This is not a hands\-on technical practitioner role. The role requires an ability to be conversant and credible in expressing business value, articulating risk considerations, and translating complex AI capabilities into compelling customer narratives. Crafting and delivering high\-impact pitch decks and proposals is essential.
Success requires a builder's mindset: generating pipeline, earning the trust and collaboration of field account teams, cultivating strong relationships with WWT's AI partner ecosystem, and consistently converting opportunities into won business. The AI Solutions Executive is expected to carry and achieve defined quota targets, integrate into the regional operating rhythm, and help enable the field sales force to position and sell AI solutions with confidence.
Responsibilities:
Pipeline Generation \& Opportunity Development
- Proactively identify, qualify, develop, and close AI opportunities across the assigned territory, leveraging new and existing customer relationships in collaboration with field sales teams.
- Build and manage a robust, multi\-stage AI pipeline with clear progression metrics from initial identification through qualification, proposal, and close.
- Conduct account\-level AI discovery sessions to uncover customer pain points, strategic priorities, and use cases that align with WWT's AI solutions portfolio.
- Drive opportunities through the full sales cycle—from initial qualification through solution development, proposal delivery, negotiation, and contract execution.
- Develop and present high\-impact pitch decks, proposals, and business cases tailored to customer\-specific requirements, clearly articulating value, differentiation, and risk considerations.
- Target both AI product/infrastructure opportunities and consulting/advisory services engagements to create multi\-phase customer AI engagements.
POD \& Cross\-Functional Collaboration
- Coordinate pursuits leveraging both dedicated and shared resources aligned to the AI GTM Teams(Regional AI PODs, shared resources, services teams, and field sales teams).
- Operate as a trusted AI subject matter resource to field sales teams, embedding into account planning and strategy sessions to surface AI opportunities.
- Build strong working relationships with field sales teams across the assigned region, earning inclusion in customer conversations and account reviews through consistent value delivery.
- Collaborate with account teams on joint customer engagements, executive briefings, and solution presentations, complementing the field team's relationship depth with AI domain expertise.
- Support field sellers in qualifying and positioning AI opportunities that originate from broader account activity, ensuring accurate scoping and appropriate solution alignment.
- Serve as the connective tissue between the centralized AI GTM organization and the regional sales teams, translating enterprise AI strategy into actionable, account\-level execution.
- Integrate with the operating rhythm and culture of the Regional sales teams to work in collaboration to set strategy and co\-sell AI solutions across the territory.
AI Partner Relationship Development
- Build and maintain strong working relationships with peer resources across WWT's AI partner ecosystem, including NVIDIA, hyperscale cloud providers (AWS, Azure, GCP), infrastructure OEMs (Cisco, Dell, HPE, etc.), and emerging AI solution vendors (e.g. Cognition, Anthropic, Snowflake, etc.).
- Leverage partner resources, co\-selling programs, and joint demand generation initiatives to accelerate pipeline creation and deal progression within assigned accounts.
- Collaborate with partner sales teams on joint customer engagements, leveraging partner technical resources, proof\-of\-concept support, and executive relationships to strengthen WWT's competitive position.
- Stay current on partner roadmaps, certifications, incentive programs, and go\-to\-market priorities to maximize WWT's participation in partner\-funded and partner\-influenced opportunities.
- Represent WWT at regional partner events, workshops, and joint customer activities, reinforcing WWT's position as a leading AI solutions partner.
Customer Presentations \& Solution Positioning
- Create and deliver compelling, executive\-quality pitch decks and proposals that communicate AI strategy, solution architecture, business value, implementation approach, and riskmitigation in clear, business\-outcome terms.
- Maintain fluency across the AI landscape: GPU/compute infrastructure, AI platforms and frameworks, GenAI and agentic AI applications, data engineering foundations, security, AI Native Engineering, workforce AI, and industry\-specific use cases.
- Articulate WWT's differentiated value in ways that resonate with CIO, CDO, CAIO, and line\-of\-business buyer personas. This includes ATC capabilities, services depth, partner ecosystem, and more.
- Tailor messaging and materials to the customer's maturity level, from early\-stage AI exploration to enterprise\-scale transformation, adjusting the conversation to address both business value and responsible AI considerations.
Operational Rigor \& GTM Contribution
- Maintain disciplined pipeline management: accurate CRM hygiene, timely stage progression, realistic forecasting, and consistent documentation of opportunity status and next steps.
- Participate in and contribute to cadence calls, pipeline reviews, and planning sessions to share market intelligence, competitive insights, and account\-level learnings.
- Provide regular reporting on pipeline health, velocity, conversion rates, and revenue/gross\-profitperformance against assigned quota targets.
- Share account\-level success stories, objection\-handling frameworks, and competitive intelligence with the centralized AI GTM team to improve organization\-wide execution.
Salary Context
This $165K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $165K to $195K.
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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