Interested in this AI/ML Engineer role at World Wide Technology?
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Qualifications & Experience
- 15+ years in cybersecurity, including 5+ years leading a security consulting practice, professional services business, or large security organization with direct accountability for people, financial results, and client outcomes.
- Demonstrated success building and scaling a practice or offering portfolio, including defining services, hiring and developing teams, and establishing go to market motions.
- Track record of executive level business development, including shaping and closing large, complex security engagements and sustaining relationships with CISOs and other executive stakeholders.
- Deep understanding of AI/ML and agentic systems and the security risks unique to them, with the credibility to lead principal level architects in security architecture, threat modeling, MLSecOps, and adversarial testing.
- Strong command of enterprise security and AI governance frameworks and the evolving regulatory environment for AI.
- Experience leading and developing senior technical talent across distributed teams, with a record of building inclusive, high performing organizations.
- Exceptional executive presence, communication, and influencing skills, with the ability to represent the practice to boards, executives, partners, and the market.
- Bachelor's degree in computer science, engineering, information security, business, or a related field, or equivalent professional experience; advanced degree preferred.
- Relevant certifications preferred, such as CISSP, CISM, CCSP, or a major cloud security specialty certification.
- Willingness to travel to client sites, partner events, and company locations as required.
Want to learn more about Consulting & Security Services? Check us out on our platform:
https://www.wwt.com/consulting-services
https://www.wwt.com/category/security-transformation
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 $175,600 to $219,500 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.
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 distributions 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 is the Solutions Consulting & Engineering (SC&E) Team and why join?
Solutions Consulting & Engineering is an organization that is Customer Focused and Solutions Led. We deliver end-to-end and emerging solutions to drive customer satisfaction, increase profitability and growth. Our success is enabled by our world-class management consulting, delivery excellence and engineering brilliance. Our goal is to bring together business acumen with full-stack technical know-how to develop innovative solutions for our clients' most complex challenges.
Job Summary
The Security Solutions Senior Director for AI Security is accountable for its strategy, growth, people, and delivery outcomes. The role sets the vision for how enterprise AI and agentic systems are secured, builds and leads project teams of principal architects and consultants who deliver that vision, and owns the offering portfolio, go to market motion, and partner ecosystem that carry it to our Security customers. It pairs practice and business leadership with credible technical depth, serving as the executive voice for AI security with client leadership, sales and solutions leaders across the org, and the broader market.
Practice Strategy & Business Leadership
- Own the vision, strategy, and multi year roadmap for the AI Security practice, defining where the practice plays across AI governance, secure AI architecture, MLSecOps, agentic system security, and AI assurance, and how it differentiates in the market.
- Own the domain's financial performance, including opportunity management, gross profit, margin, utilization, and pipeline health, setting annual targets with Security Solutions leadership and managing the practice to plan.
- Define and evolve the AI security offering portfolio, including service definitions, delivery models, pricing, and packaging, keeping pace with change in AI platforms, agent frameworks, and the regulatory landscape.
- Prioritize investment in methodologies, accelerators, reference architectures, and lab environments, and sponsor development of the intellectual property that shortens delivery and raises quality.
- Represent AI security in Security Solutions planning and operating rhythms, providing forecasts, capacity plans, and risk visibility to senior leadership.
Go to Market & Client Executive Engagement
- Serve as executive sponsor for strategic client relationships, engaging CISOs, CIOs, Chief Data Officers, and boards on AI risk, secure adoption strategy, and investment priorities.
- Lead the pursuit of large and complex AI security opportunities, shaping deal strategy, solution approach, and commercial terms, and serving as the executive escalation point across the sales cycle.
- Partner with sales leadership and account teams to build and manage the AI security pipeline, enabling sellers with positioning, qualification criteria, and client facing collateral.
- Represent the practice externally through conferences, industry forums, and published points of view that establish market credibility and generate demand.
Team Leadership & Talent Development
- Build, lead, and develop a high performing team of principal architects, lead consultants, and senior consultants, owning hiring plans, career paths, performance management, and succession.
- Set clear expectations and standards for the practice, holding leaders accountable for delivery quality, client outcomes, and the professional growth of their teams.
- Develop the next generation of AI security leaders through coaching, stretch assignments, and structured skills development across AI, cloud, and security domains.
- Foster a culture of technical excellence, curiosity, and collaboration that attracts and retains top talent in a highly competitive market.
Delivery Governance & Quality
- Hold ultimate accountability for delivery outcomes across the domain's portfolio of engagements, including scope, quality, profitability, and client satisfaction.
- Establish and govern delivery standards, quality gates, and engagement risk reviews, and serve as the executive escalation point for delivery issues and client concerns.
- Sponsor the AI security reference architectures, control standards, and pattern library maintained by the practice's principal architects, ensuring they remain current, reusable, and aligned with frameworks.
- Ensure delivery teams have the tooling, lab environments, and enablement needed to assess and secure AI and agentic systems at scale.
Technical Direction & Thought Leadership
- Set the technical direction for the domain, maintaining a credible and current point of view on threats to AI systems, including prompt injection, model supply chain compromise, data poisoning, adversarial attacks, agent identity and authorization failures, and tool and MCP supply chain risk, and on the controls that address them.
- Track developments in AI platforms, agent frameworks, AI security tooling, regulation, and translate them into practice strategy, offerings, and client guidance.
- Author and sponsor thought leadership, frameworks, and executive briefings that shape how clients and the industry approach AI security.
- Participate in industry bodies and standards efforts relevant to AI security, bringing that perspective back into the practice.
Partner Ecosystem & Cross Practice Collaboration
- Own the AI security partner strategy, building executive relationships with AI security vendors, hyperscalers, and model providers, and driving joint solutions, enablement, and joint selling motions.
- Integrate AI security into adjacent practices and offerings, including AI and data, cloud, infrastructure, and the broader cybersecurity portfolio, so that security is embedded in every AI engagement the firm delivers.
- Where applicable, partner with leaders across functional teams to embed security into AI system design, development, and operations.
Salary Context
This $175K-$219K range is above 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 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($197K) sits 7% below the category median. Disclosed range: $175K to $219K.
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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