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About This Role
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Information Security, or a related field or equivalent hands\-on experience.
- Minimum 8 years of experience in security engineering, information security, or related technical disciplines, with a demonstrated focus on securing AI/ML systems.
- Proven experience defining and executing enterprise security strategies.
- Experience and deep technical knowledge of how AI/ML technologies work.
- Deep familiarity with AI security frameworks (e.g., NIST AI RMF, MITRE ATLAS, OWASP).
- Experience securing organizational tool stacks including cloud\-based, hybrid or on\-prem solutions.
- Deep knowledge of AI related threats, risks and mitigation strategies.
- Strong foundation in core security principles—threat modeling, secure design, and identity and access control—applied to AI/ML systems and the infrastructure that supports them.
- Hands\-on experience securing AI/ML systems, including practical AI red teaming against LLMs, agentic workflows, or RAG systems
- Strong programming and scripting proficiency (e.g., Python) for building security tooling, automation, and test harnesses across the AI/ML lifecycle.
- Hands\-on experience securing cloud and containerized environments (AWS, Azure, and/or GCP; Kubernetes and containers), including the infrastructure on which AI/ML workloads are trained, deployed, and served.
- Experience embedding security into MLOps and AI/ML development pipelines (and the CI/CD workflows that feed them), including model and dataset integrity checks, guardrail validation, and automated security testing.
- Proven ability to communicate complex security risk clearly to both technical and non\-technical audiences, including engineers, leadership, and customers.
- Demonstrated experience mentoring engineers and serving as a senior technical authority and subject\-matter expert.
- Hands\-on offensive security and AI red\-teaming experience targeting AI systems—including prompt injection, jailbreaks, data poisoning, model extraction, and agent/tool\-abuse techniques—along with familiarity testing the web, API, and infrastructure layers these systems depend on.
- Applicants must be authorized to work in the United States without sponsorship. We are unable to provide sponsorship now or in the future for this position.
Preferred Qualifications
- Hands\-on experience with AI/ML security tooling and AI security posture management (AI\-SPM) platforms (e.g., Wiz AI\-SPM, Protect AI, HiddenLayer, Lakera, or comparable model\-scanning and guardrail solutions), complemented by familiarity with broader cloud and enterprise security platforms.
- Hands\-on experience securing AI/LLM\-enabled and agentic applications, including deep familiarity with OWASP Top 10 for LLM Applications, OWASP Top 10 for Agentic AI, MITRE ATLAS, NIST AI RMF, and Model Context Protocol (MCP) security implications.
- Practical experience with agent frameworks and orchestration (e.g., LangChain/LangGraph, CrewAI, AutoGen, or custom agent architectures) and with LLM evaluation, observability, and red\-teaming tooling for testing model and agent behavior.
- Experience operating within governance, risk, and compliance frameworks, with exposure to enterprise AI adoption, third\-party/vendor AI risk assessment, and AI policy or standards development.
- Experience in client\-facing, consulting, or pre\-sales roles, translating AI security concepts for customer and executive audiences through briefings, workshops, or advisory engagements.
- Industry certifications such as CISSP, OSCP, or AWS/Azure/GCP security certifications; emerging AI\-security\-specific credentials (e.g., AI red\-teaming or AI/LLM security certifications) are especially valued.
Certain states and localities require employers to post a reasonable estimate of the salary range. A reasonable estimate of the current base pay range for this position is $116,000 to $145,000 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 are 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
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:
*We are not able to offer visa sponsorship, 1099 status, or work with C2C for this role**.*
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 is the Internal WWT IT Team, and why join?
The Internal WWT IT team is the backbone of our company's technological infrastructure, ensuring seamless operations and continuous innovation. Our team is dedicated to managing and supporting the company's technology infrastructure, ensuring the smooth operation of hardware, software, networks, and data systems, while providing top\-notch technical support to employees.
By joining the Internal WWT IT team, you will play a crucial role in maintaining the efficiency and security of our IT environment, enabling the company to achieve its strategic goals. The Internal IT team offers the opportunity to work in a dynamic and collaborative environment, where your contributions will have a direct impact on the company's success. If you are passionate about technology and eager to take on new challenges, we encourage you to apply and join our team.
World Wide Technology's Information Security organization is hiring a Senior AI Security Engineer to help secure the organization's AI and machine learning ecosystem and reduce risk across WWT\-developed and WWT\-operated AI systems. As a Senior AI Security Engineer, you'll be a hands\-on technical contributor helping mature an evolving AI security function. Your focus is executing the highest\-impact technical security work and enabling teams to build and deploy AI systems securely at speed through threat modeling, model and AI application security review, adversarial testing and red\-teaming, security tooling and automation, and vulnerability remediation across the AI/ML lifecycle. Beyond securing what we build, you'll help govern the safe adoption of AI across the enterprise, establishing standards for sanctioned AI use, evaluating third\-party AI tools and vendors, and reducing the risk of shadow AI. You'll support our customer\-facing teams, equipping them to educate customers on AI security best practices and serving as a technical resource in customer conversations. You'll also help grow the skills of those around you and act as a trusted AI security resource for teams across the organization.
Key Responsibilities
- Threat model AI/ML systems across the lifecycle (data ingestion, training, fine\-tuning, deployment, inference).
- Assess and mitigate AI\-specific attack classes such as prompt injection, jailbreaks, data poisoning, adversarial examples, and insecure agent and tool\-use patterns.
- Secure the ML pipeline and supply chain, including model provenance and the evaluation of third\-party and open\-source models.
- Advise on compliance alignment (e.g., NIST AI RMF, EU AI Act, OWASP).
- Establish AI security policies, standards, and secure\-development guidance for teams building with AI.
- Partner with detection \& response teams to extend monitoring and incident response to AI\-related incidents.
- Help govern sanctioned vs shadow AI use; review and harden 3rd\-party AI vendors and integrations.
- Create and monitor controls around AI prompts, data movement and AI guardrails across the AI stack for the organization.
- Continuously monitor the AI threat landscape and translate research into actionable engineering mitigations.
- Partner with IT and cross\-functional teams outside of Information Security offering technical expertise around secure use and adoption of AI.
- Enable customer\-facing teams by developing AI security guidance and reference materials, supporting customer briefings and workshops, and serving as a technical subject\-matter expert in customer engagements.
Salary Context
This $116K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $116K to $145K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
World Wide Technology AI Hiring
World Wide Technology has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, San Francisco, CA, US, New York, NY, US. Compensation range: $80K - $235K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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