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About This Role
Description
Job Summary
Join CDW and help protect delivery of full stack technology solutions and global services for 250K+ customers—including corporate enterprise, government, education, and healthcare industries. You will be on a team dedicated to collaborative delivery of a new global information security strategy, operating model, and objectives to accelerate CDW’s business goals in a secure way.
Your role at CDW is of the utmost importance to the company’s mission, objectives, and reputation. As the Principal AI Security Engineer, you will be the technical authority on keeping AI secure across the enterprise. At the heart of this role is assessment: you'll own how CDW evaluates AI risk before it reaches production, then design and build the controls that close it. You’ll partner across our security teams and platform teams to define the guardrails, identity model, and detection capabilities that let CDW use AI aggressively and securely, whether we build it, buy it, or need to block it.
This is a hands-on principal role. You will set enterprise AI security direction and help build what you design: reference architectures, controls, and tooling. You will also serve as a trusted advisor across CDW, helping senior leaders and AI governance teams make practical, risk-informed decisions as we scale AI adoption.
Your impact: fewer AI-related incidents, faster and safer AI adoption, and security controls that scale with the business.
What you will do
- Own CDW's AI security assessment program. Design and mature the process, criteria, and risk tiers used to approve, restrict, monitor, or block AI use cases, platforms, vendors, and shadow AI.
- Lead security assessments of AI systems, agents, and integrations before and after deployment, and set the security bar other teams build to.
- Partner with the AI Council, AppSec, and platform teams to build assessment findings into AI use case reviews, so risks are found and addressed before deployment.
- Evaluate third-party AI vendors, purchased AI capabilities, and unapproved AI use against CDW's AI security requirements and guardrails.
- Define methods and tools for AI red teaming, including automated adversarial testing, to validate assessment findings and test controls under real attack conditions.
- Assess AI-related controls across CDW's existing security stack, find gaps, and drive root-cause remediation rather than one-off fixes.
- Define and own CDW's enterprise AI security architecture, including standards, reference architectures, and secure design patterns aligned to NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLM Applications, and MITRE ATLAS.
- Set security requirements across the AI lifecycle, from data handling and model selection through deployment, monitoring, and retirement.
- Define trust boundaries, authorization models, and least privilege for single-agent and multi-agent systems.
- Design end-to-end security for the AI stack, including LLM inference pipelines, AI agents, MCP-based tool orchestration, and agentic workflows.
- Design and deploy guardrails, sandboxes, runtime controls, and AI gateway capabilities, including prompt and response inspection, data loss prevention, rate limiting, and circuit breaking.
- Build or implement ways to track and govern AI identities, including agents, service principals, workload identities, and delegated access, so CDW always knows what access every AI system has.
- Partner with Identity Engineering teams to align AI identities to CDW's enterprise lifecycle, ownership, and least privilege standards.
- Partner with Detection Engineering and Security Operations to build detections, telemetry, and response playbooks for prompt injection, jailbreaks, data leakage, model abuse, and tool misuse.
- Work with engineering, data science, product, and infrastructure teams to build security in from design through production and publish practical guidance that makes the secure path the easy path.
- Present risk assessments, recommendations, and investment priorities to senior leadership and security governance bodies in clear, business-focused terms.
- Mentor security engineers and architects to grow AI security expertise across CDW.
Success in This Role Looks Like
- Every AI use case, platform, and vendor is assessed against consistent, risk-tiered criteria, with clear approve, restrict, monitor, or block decisions.
- Fewer AI-related security incidents, with detections and playbooks in place for faster containment when they happen.
- Teams know what to expect from AI security assessments, and reviews move fast enough that security speeds up AI adoption instead of blocking it.
- Unapproved AI use is visible, and is either brought under governance or blocked.
- Every AI identity and agent has a known owner, defined access, and least privilege.
- Approved AI reference architectures and guardrails that teams adopt by default.
- Clear, data-driven visibility into AI security posture, control gaps, and remediation progress.
What we expect of you
- Bachelors degree and 10+ years in security engineering or security architecture, including hands-on work securing AI/ML systems, or 14+ years of experience in security engineering or security architecture, including hands-on work securing AI/ML systems.
- Proven experience designing and leading security assessments of AI systems, platforms, or vendors, and turning findings into clear, risk-based decisions.
- Hands-on experience securing production AI technologies, including at least one of the following: + Deploying AI guardrails or sandboxes + Managing AI or agent identities + Detecting or mitigating prompt injection + Implementing AI security tooling, built or bought
- Experience building or evaluating security for production AI systems, beyond research or theory.
- Solid understanding of how LLMs work, including pretraining, fine-tuning, RLHF, inference, retrieval-augmented generation, and safety alignment, and how each layer can be attacked.
- Experience securing AI platforms and agent frameworks such as Microsoft Copilot Studio, Azure AI services, OpenAI or Anthropic tool use, MCP-based integrations, and frameworks such as Semantic Kernel or LangChain.
- Experience securing AI deployments in public cloud (Azure, AWS, or GCP), identity and access management, and zero trust principles.
- Proven ability to influence senior leaders and cross-functional teams without direct authority.
- Experience on a security team defending production AI systems at scale, or at a frontier AI lab, a plus.
- Background in Infrastructure, Cloud, Platform, or Corporate Security, Security Operations, or Detection and Response, a plus.
- Software security review, secure SDLC, vulnerability discovery, or offensive security experience, a plus
- Published work, conference talks, CTF results, or production red team work in prompt injection, jailbreak research, or adversarial ML, a plus.
- Experience with AI security posture management, AI gateways, or AI observability platforms, a plus.
- Relevant certifications such as CISSP, CCSP, OSCP, or AI security credentials, a plus.
Pay range: $172,000-240,000 depending on experience and skill set
Annual bonus target of 15% subject to terms and conditions of plan
Benefits overview: https://cdw.benefit-info.com/
Salary ranges may be subject to geographic differentials
CDW is committed to being an AI-fluent organization
We’re looking for people who bring curiosity, a learner’s mindset, and a willingness to engage with ever-evolving technology and tools. We value adopting AI as a partner, openness to experimentation, and a shared interest in learning together on AI. Our goal is to create a culture where AI enhances—not replaces—human creativity and decision-making. You don’t need to be an expert today; what matters is your readiness to explore, adapt, and grow with us as we integrate AI responsibly and effectively into our work. Additionally, CDW is committed to fostering an equitable, transparent, and respectful hiring process for all applicants. During our application process, our goal is to understand your experience, strengths, skills, and qualifications. As an AI forward company, we see AI not just as a tool, but as a catalyst for new ways of thinking, creating, and communicating. We encourage candidates to embrace an AI mindset, one that’s curious, adaptive, and ready to explore what’s possible. We welcome thoughtful use of AI to expand your perspective and elevate how you share your story, while ensuring your application remains rooted in your own background, judgment, and voice.
About Us
CDW is a Fortune 500 technology solutions provider that helps businesses, government, education, and healthcare organizations achieve what’s possible through technology. What makes CDW different isn’t just what we do—it’s how we do it. At CDW we act as one—building trust, speaking candidly, and working together to achieve more. We play to win—focusing on what matters most and delivering for our customers. And we think forward—staying curious, moving fast, and continuously learning. We believe meaningful work happens when people feel supported, heard, and empowered to contribute. That’s why we think of ourselves as coworkers, not just employees—working together to solve complex challenges and deliver real impact for our customers and communities.
As a full‑stack, full‑lifecycle technology partner, CDW brings deep expertise, strong relationships, and broad industry knowledge to help turn ideas into outcomes. When you join CDW, you become part of a collaborative environment where your work matters, your growth is supported, and your contributions help shape what’s next.
Together, we deliver the full promise of what technology can do. Together, we Make Amazing Happen.
CDW is an equal opportunity employer. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status or any other basis prohibited by state and local law.
Salary Context
This $172K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CDW, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $172K to $240K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CDW AI Hiring
CDW has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Chicago, IL, US, Remote, US, Illinois, United States. Compensation range: $158K - $240K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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