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About This Role
Qualifications:
- Minimum 10 years in technology consulting, services sales, or practice management at a solutions provider, consultancy, or systems integrator.
- Direct experience scoping and selling data, AI, analytics, or cloud consulting engagements. Must understand services economics: SOW development, effort estimation, margin dynamics, and utilization.
- Practical knowledge of enterprise AI use cases at the engagement level with the ability to define workshops, data readiness assessment, and/or an operational acceleration program with specific activities and deliverables.
- Understanding of the coding assistant (AI Native Engineering) and workforce AI landscape. Ability to scope developer productivity assessments, coding assistant pilots, adoption services, and ROI engagements.
- Working knowledge of data engineering, governance, cloud data platforms, data foundations, and security solutions required for enterprise AI.
- Comfortable developing service proposals during the sales cycle and presenting methodology to customers in competitive evaluations.
- Experience collaborating across sales, architecture, and delivery organizations to ensure scope fidelity and clean handoffs.
- Bachelor's degree required.
Preferred Qualifications:
- Prior experience at a consulting firm in a client\-facing, services sales or practice development role.
- Experience designing engagements that leverage lab/PoC environments for customer validation.
- Prior delivery management experience in addition to services sales/scoping.
Regional travel up to 40%
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 $170,000\.00 to $190,000\.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 Services Consultant is the consulting pre\-sales partner within each regional AI POD. This role develops, scopes, and positions the consulting services engagements that translate AI strategy into funded project across all AI GTM categories.
This role is critical because AI infrastructure investment decisions are closely linked to the services that enable organizations to unlock AI's potential business value while managing risk. Increasingly AI initiatives are gated by the customer's ability to prove value through use cases, demonstrate data readiness, and operationalize AI workloads.
The AI Services Consultant develops and packages the services engagements that remove these gates and accelerate the customer's path from exploration to production. Additionally, this role develops and positions workforce AI and coding assistant services that represent a high\-growth, high\-attach consulting opportunity.
Success requires a blend of services business acumen, AI use\-case expertise, workforce AI fluency, and the ability to operate in the space between sales and delivery to develop service proposals during the sales cycle, present methodology to customers, ensure clean handoff to delivery teams, and build the next opportunity off the success of each services engagement.
Responsibilities:
Consulting Services Development \& Positioning
- Position and conduct initial discovery to scope consulting services engagements across all AI GTM categories (AI Factory, AI Foundations, Workforce AI, and AI Native Engineering).
- Service motions include AI security, data readiness \& governance, use\-case acceleration, AI lab services (ATC\-hosted), operational acceleration programs, AI Factories (design, build, operate), workforce adoption \& innovation, and AI Native Engineering (e.g. Coding Assistants).
POD \& Cross\-Functional Collaboration
- Operate as an integrated member of the three\-person regional POD (AI Solutions Executive, AI Solutions Architect, AI Services Consultant), participating in weekly POD cadence, joint pipeline reviews, and account planning.
- Partner with the AI Solutions Executive to attach consulting services to every AI opportunity—whether infrastructure\-led or workforce AI\-led—creating multi\-phase engagement models that expand account penetration.
- Collaborate with the AI Solutions Architect and SSAs to define the right entry\-point services (assessments, workshops, roadmaps, pilots) and to sequence multi\-phase programs that expand over time.
- 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.
- Present consulting methodology, approach, and value proposition to customer stakeholders during competitive evaluations where services differentiation is the deciding factor.
- 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 Factory Justification \& Consumption Acceleration
- Develop services to drive business case models, TCO analyses, and consumption forecasting that justify GPU/compute infrastructure investment tied to measurable business outcomes.
- Design lab services engagements leveraging WWT's ATC for proof\-of\-concept, performance benchmarking, and reference architecture validation that de\-risk AI Factory decisions.
- Track consulting services pipeline, attach rates, and delivery outcomes—demonstrating the relationship between consulting engagement and downstream infrastructure consumption.
Workforce AI \& Developer Productivity Services
- Position and conduct initial scoping for developer productivity assessments, coding assistant vendor evaluation, pilot program design and execution, adoption metrics and ROI measurement, and security/governance frameworks for AI\-generated code and content.
- Stay current on the workforce AI competitive landscape: Microsoft 365 Copilot, GitHub Copilot, ServiceNow AI agents, Google Gemini, and emerging platforms (Anthropic, Cognition, Cursor, \+).
Services Growth
- Capture customer feedback, and engagement outcomes that inform service package iteration and improvement.
- Build additional pipeline on the success of each service engagement delivered to the customers.
AI Transformation Program Structuring \& Continuity
- Collaborate with AI Solutions Architects and SSAs to structure multi\-phase AI consulting engagements that extend beyond initial scopes, sequencing use case validation, data readiness, platform enablement, and scaled adoption to drive long\-term customer value.
- Develop program\-level roadmaps that align business outcomes, technical milestones, and organizational readiness, enabling customers to transition from isolated AI initiatives to sustained, production\-scale adoption.
- Co\-develop with SSAs lightweight governance and operating models required to operationalize AI initiatives, including roles, decision frameworks, and execution cadence aligned to customer maturity and organizational structure.
- Maintain continuity between pre\-sales and early delivery phases by ensuring alignment between proposed solutions, delivery approach, and expected business outcomes, reducing re\-scoping risk and improving execution quality.
- Identify repeatable patterns across consulting engagements and contribute to the development of reusable service frameworks, playbooks, and accelerators that improve consistency, scalability, and margin performance.
Salary Context
This $170K-$190K 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: $170K to $190K.
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
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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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