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About This Role
Required
- Bachelor's Degree in Arts/Sciences (BA/BS) or Related Field. A combination of education, training and experience may be considered in lieu of a degree.
- A minimum of 10 years' progressive experience in a related field
- Minimum of 5 years active Program Management experience.
- Minimum 10 years of Project Management experience.
- One to three years prior sales/business development experience is preferred
- Project Management Professional (PMP) certification through Project Management Institute (PMI)
- 8+ years in program or project management, with at least 5 years leading on-site delivery of large infrastructure, data center or field deployment programs.
- Personally led sites from kickoff through hand-off, including the first three weeks, with mixed crews of employees, contractors, partner labor and customer staff.
- Has trained and ramped project staff by hand in a live delivery environment. Writing the documentation does not count.
- Disciplined about governance and evidence: receiving standards, SOP rollout, DOA and RMA handling, expedites, daily reporting, gate readiness.
- The standing to direct senior program managers and set expectations with customer and partner leadership on day one.
- Willing to travel internationally on short notice for up to three weeks at a stretch - must be able to travel 60% of the time
- Must have valid Passport
- Salesforce CRM software experience.
- Experience managing, tracking, and forecasting the financials and budgets for large scale programs.
- Understands expectations and reporting requirements needed to provide the level of visibility and clarity on the financial health of the program.
- Excellent understanding of the contract process.
- Able to manage contract modifications, control the impact to a program, and take the lead for negotiations on contract scope, cost, and schedule.
- Good understanding of Supply Chain processes.
- Ability to work independently and aggressively track to program timelines.
- Strong team skills.
- Strong communication skills (written, verbal, and presentation).
Preferred
- NVIDIA AI Factory, DGX or NVL rack deployment experience, or familiarity with the NVIS curriculum.
- Background in hyperscale or colocation data center build-outs, HPC cluster deployments, large multisite refresh programs, or field deployment programs with heavy logistics.
- Delivery experience in ANZ or EMEA. PMP or equivalent. Multisite bootcamp graduate or instructor.
- Has been the first person into a new site or a new program.
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.
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base salary range for this position is $132,400 - $165,500. Actual salary will be based on a variety of factors, including 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 salary.
If you have any questions or concerns about this posting, please email [email protected].
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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!
Job Summary
The Strategic Program Manager is responsible for managing large scale transformational client initiatives, which will require him/her to successfully engage and work across multiple technical and global teams within WWT and potentially with the client. The ideal candidate will have proven leadership capabilities and experience managing technology programs with a minimum budget of $25MM or greater. He/she will have managed large distributed teams of matrixed resources of at least 50 people or more and have strong presentation and public speaking skills. He/she will have experience developing and estimating client statements of work.
This is the PMO seat on the Tiger Team. You own governance and reporting for the first two weeks of every new AI Factory deployment. You arrive before build starts, get the program controls running before anyone touches hardware, teach the PMO part of the curriculum to the incoming project team, and hand a working governance structure to the permanent program manager at the two-week mark. It is a working role, not an oversight role. We want someone who has personally run the messy first weeks of large on-site builds, has trained project staff by doing the work next to them, and can set expectations with senior PMs, customer stakeholders and partner leads on day one.
Responsibilities
- Defines and implements governance and controls across the program, including but not limited to: status reporting, risks and issues management and reporting, stakeholder engagement and reporting, other routines and artifacts as required in order to control the program
- Extensive problem-solving skills, with the fortitude to drive resolutions to a close
- Extensive knowledge of service delivery models focused on continuous improvement and adapting to the changing landscape of the service industry.
- Monitors and manages the program level milestones, risks, issues, and decisions
- Drives decision making processes where and when appropriate
- Works with leadership and technical leads to staff and resource the program appropriately
- Escalates and drives program level issues resolution and risk mitigation
- Responsible for managing all budgeting, forecasting and program financials to meet financial expectations as outlined in the program
- Responsible for managing program level procurement reporting and visibility
- Ensure roles and responsibilities are defined and align to staffing plans
- Responsible for end-to-end program delivery and quality
- Drives stakeholder engagement to ensure appropriate level of communication and decision making for the program
- Creates executive reporting, and delivers the reports to the appropriate executive stakeholders, as defined by the program
- Ensures the program is aligned to the statement of work.
- Works with the appropriate leadership and team members to define the statement of work and any changes
- Monitors and assesses customer satisfaction, quality, and profitability.
- Project managers assigned to the program will report to the program manager
- Coaches and mentors the PMs and other team members as needed, to achieve the optimal productivity and highest quality of deliverables.
- Creates and manages long term goals for the program or portfolio of projects.
- Responsible for ensuring the successful onboarding and offboarding of program resources
- Acts as a liaison between program staff and upper management/executive
Before the build starts
- Stand up the governance and reporting structure before build starts: daily reporting, gate evidence, RAID and decision logs, escalation paths, and the tracking that feeds the AI Factory tool.
- Roll out WWT's standard SOPs and receiving standards the same way at every site. Nothing gets improvised locally.
- Open the DOA, RMA and expedite registers with the Logistics lead so material problems are visible and worked from day one.
Training the crew
- Teach the PMO track of the curriculum by hand to the incoming project team. Many of them will be new hires, contractors or partner staff who have never run a program like this.
- Confirm person by person that it landed, and track it, before anyone owns live reporting or gate evidence.
- Coach the permanent program manager through their first gates and first customer status reviews. Do not run them for them.
Hand-off
- Work next to the permanent project team from day one so the customer relationship sits with them, not with you.
- Hand off at about two weeks, documented, with no Tiger Team extension needed.
- Escalate fast when a site is drifting. On hybrid sites, review the reporting and evidence daily and be ready to fly in.
Feeding the curriculum
- Carry what each site taught you back into the curriculum, SOPs and reporting templates.
- Join the Tiger Team resets between deployment waves and the formal NVIS training in Sydney.
- Grow the PMO bench behind you by having future Tiger Team members shadow you.
Salary Context
This $132K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $132K to $165K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
World Wide Technology AI Hiring
World Wide Technology has 14 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Maryland Heights, MO, Harrow, England, UK. Compensation range: $92K - $219K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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