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About This Role
Qualifications:
The ideal candidate is a commercially minded, customer-obsessed business developer who can operate across strategy, sales, technology, and partner ecosystems. They are comfortable engaging an executive sponsor one minute and being a part of a complex pursuit team the next. They understand that Workforce AI value is realized through the combination of the right technology, relevant use cases, secure data, effective change management, and sustained adoption.
The candidate should be:
- Experienced in building business development campaigns using a consultative approach with a demonstrated record of building pipeline and closing complex technology opportunities.
- Fluent enough in AI and enterprise technology to ask strong questions, challenge assumptions, and collaborate effectively with technical specialists.
- Skilled at translating technical capabilities into business outcomes, financial rationale, and an executive-ready story.
- Effective at navigating matrixed organizations and coordinating customers, OEMs, partners, sales teams, consultants, architects, and delivery leaders.
- Comfortable working in a fast-moving market where product roadmaps, commercial models, and competitive dynamics change quickly.
- Self-directed, organized, resilient, and accountable for measurable outcomes.
Qualifications
Education
- Bachelor's degree in business, technology, marketing, computer science, or a related field preferred.
- A combination of education, training, and relevant experience may be considered in lieu of a degree.
Work experience
- 5+ years of progressive experience in enterprise technology sales, business development, management consulting, software, cloud, digital workplace, AI, or a related field.
- Demonstrated success selling complex, multi-stakeholder technology solutions with measurable pipeline and revenue results.
- Experience working with one or more enterprise AI, productivity, knowledge, collaboration, cloud, or software platforms.
- Experience building and managing relationships with OEMs, strategic partners, channel organizations, and distribution ecosystems.
- Experience leading customer discovery, executive presentations, workshops, proposals, negotiations, and complex deal pursuits.
- Experience developing business cases, ROI/TCO analyses, value frameworks, or financial justification for technology investments.
Knowledge, skills, and abilities
- Working knowledge of enterprise Workforce AI concepts, including AI assistants, enterprise search, retrieval-augmented generation, agents, workflow automation, productivity, employee support, and adoption at scale.
- Familiarity with enterprise offerings from Glean, Microsoft 365 Copilot, Google Gemini Enterprise, Anthropic, OpenAI, and comparable AI providers or platforms.
- Understanding of the factors that affect enterprise AI adoption: identity, permissions, data quality, security, privacy, compliance, governance, integration, user experience, training, and change management.
- Strong executive communication, facilitation, storytelling, negotiation, and relationship-building skills.
- Strong business acumen and financial analysis skills, including the ability to connect investments to customer outcomes.
- Ability to flourish with minimal guidance, be proactive, handle ambiguity, and make sound decisions in a rapidly evolving market.
Travel and working conditions
- Travel may be required for customer meetings, partner engagements, WWT events, workshops, and internal collaboration.
- The role requires the ability to work effectively with distributed, cross-functional teams and to manage multiple active customer and partner pursuits.
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 $145,000.00 to $160,000.00 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.
The well-being of WWT employees is essential. 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 (Medical & Prescription), Dental, and Vision Care, Onsite Health Centers (MO & IL), Employee Assistance Program, Wellness program
- Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement
- Paid Time Off: PTO & Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring
- Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program
Note: This is not an all-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US benefits website at wwt.com/us-benefits.
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 require accessibility accommodation(s) or adjustment during any stage of the hiring
process, please let your WWT Recruiter know. The recruiter will work with you to understand your needs and help ensure an accessible experience throughout the interview process.
World Wide Technology is an Equal Opportunity Employer.
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Requirements: Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
Founded in 1990, WWT brings together strategy, deep technical expertise and world-class
partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state-of-the-art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distribution capabilities.
With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.
Want to work with highly motivated individuals on high-performance teams? Join WWT today!
What will you be doing?
Workforce AI is moving from experimentation to enterprise-scale adoption. Customers are evaluating a growing portfolio of enterprise AI assistants, search and knowledge platforms, agentic tools, and foundation-model offerings — including Glean, Microsoft 365 Copilot, Google Gemini Enterprise, Anthropic, OpenAI, and related technologies.
The Workforce AI Business Development Manager will help customers determine where these technologies can create meaningful business value, how to select and sequence the right solutions, and what is required to move from pilot to secure, adopted, measurable production use. This role will build demand, shape strategy, orchestrate WWT's matrixed resources, and drive profitable growth across the Workforce AI portfolio.
The successful candidate combines enterprise business development, consultative selling, partner leadership, and enough technical fluency to engage credibly with business, technology, data, security, and employee-experience leaders. The role is not limited to selling a single product; it requires helping customers make informed buy, build, and scale decisions across a rapidly changing OEM landscape.
High-Level Responsibilities
The Workforce AI Business Development Manager will:
- Build qualified pipeline and profitable revenue through new-logo, expansion, cross-sell, and strategic partner-led opportunities.
- Align WWT account teams, solution architects, consultants, services leaders, OEM field teams, distributors, and delivery resources around a common pursuit strategy.
- Translate customer workforce challenges into outcome-oriented opportunities spanning AI assistants, enterprise search, knowledge discovery, productivity, workflow automation, agents, employee support, and adoption services.
- Lead consultative discovery, executive briefings, use-case prioritization, value assessment, and roadmap conversations.
- Help customers compare and sequence solutions such as Glean, Microsoft 365 Copilot, Google Gemini Enterprise, Anthropic, OpenAI, and other relevant enterprise AI offerings based on business fit, data readiness, security, governance, integration, adoption, and economics.
Key Responsibilities
Growth and customer engagement
- Build the Workforce AI account, and partner strategy; generate qualified pipeline and maintain disciplined opportunity reviews and forecasts.
- Lead customer discovery, executive briefings, and roadmap sessions across IT, digital workplace, HR, operations, and business functions.
- Translate customer priorities into business cases, ROI/TCO models, success metrics, adoption plans, proposals, and executive-ready solution narratives.
OEM and partner leadership
- Develop relationships with Glean, Microsoft, Google, Anthropic, OpenAI, and other relevant OEM and channel stakeholders.
- Coordinate joint account planning, co-selling, demand generation, partner funding, enablement, and executive engagements.
- Maintain current knowledge of OEM roadmaps, commercial models, licensing, competitive positioning, and implementation considerations.
Solution shaping and deal execution
- Guide customers through buy-versus-build and multi-OEM decisions, incorporating security, privacy, data, governance, integration, adoption, and change-management requirements.
- Orchestrate WWT's sales, consulting and services teams from qualification through close and transition to delivery.
- Protect customer outcomes, solution quality, commercial discipline, and expansion potential throughout the pursuit lifecycle.
Enablement and market development
- Create repeatable sales plays, discovery guides, battlecards, customer stories, and internal enablement for account teams.
- Capture customer, partner, and market insights to improve WWT's offerings, messaging, go-to-market priorities, and thought leadership.
Salary Context
This $145K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $145K to $160K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
World Wide Technology AI Hiring
World Wide Technology has 18 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, MLOps Engineer. Positions span Remote, US, Maryland Heights, MO, Dallas, TX, US. Compensation range: $92K - $190K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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