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About This Role
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Job Title: Cloud \& AI Security Engineer
Bonita Springs, FL, USA, 34134
Employment Status: Full\-time
Posting Start Date: 7/15/26
Founded in 1965, Herc Rentals is one of the leading equipment rental suppliers in North America with 2025 total revenues reaching approximately $4\.4 billion. Herc Rentals’ parent company, known as Herc Holdings Inc., listed on the New York Stock Exchange on July 1, 2016, under the symbol “HRI.” Herc Rentals serves customers through approximately 609 locations and has about 9,700 employees in North America as of March 31, 2026\.
Job Purpose
Herc Rentals is one of the largest equipment rental suppliers in North America, supporting construction, industrial, and infrastructure customers nationwide. Our Cybersecurity team protects the people, customers, systems, and data that keep that business running.
Joining us means working with a modern, cloud\-first security stack, an emphasis on AI and automation, a collaborative team, and real opportunities to grow your career in cloud security engineering.
Short Description
Herc Rentals is seeking a skilled and forward\-thinking Cloud \& AI Security Engineer to join our Cybersecurity team. In this role, you will be responsible for designing, implementing, and maintaining secure cloud infrastructure across our enterprise environment, with a strong emphasis on leveraging artificial intelligence and automation to enhance our security posture and protecting against advanced threats.
You will serve as a key technical contributor in protecting Herc Rentals' cloud\-hosted systems, data, and applications from evolving cyber threats while ensuring alignment with regulatory requirements and industry best practices.
What you will do...
Cloud Security Architecture and Engineering
Design and implement security controls, policies, and guardrails across cloud environments, including Amazon Web Services (AWS), Microsoft Azure, and related platforms.
Develop and maintain secure cloud architecture patterns for infrastructure, applications, and data pipelines.
Configure and manage cloud\-native security services including identity and access management (IAM), encryption, network segmentation, and logging.
Conduct regular security assessments, architecture reviews, and threat modeling for cloud workloads.
AI and Automation Integration
Implement data governance and data loss prevention (DLP) controls for AI systems to prevent sensitive data — including customer PII, contracts, and financial information — from leaking into prompts, model training data, or third\-party model APIs.
Discover, assess, and govern the use of unsanctioned AI and SaaS tools ("shadow AI") across the organization, establishing approval, monitoring, and offboarding processes to reduce data exposure and compliance risk.
Define and enforce safe data\-handling standards for prompt and model\-response content across internally built and vendor\-provided AI services.
Leverage AI and machine learning tools to enhance threat detection, anomaly detection, and automated incident response capabilities.
Build and maintain security automation workflows to reduce manual effort and accelerate response times across the security operations lifecycle.
Evaluate and implement AI\-powered security platforms and tools, ensuring they are deployed securely and aligned with Herc Rentals' standards.
Identify opportunities to apply AI\-driven insights to improve vulnerability management, access governance, and cloud compliance monitoring.
Security Operations and Incident Response
Monitor cloud environments for security events, misconfigurations, and anomalous activity using SIEM and cloud\-native monitoring tools.
Participate in incident response activities, including investigation, containment, remediation, and post\-incident review for cloud\-related security events.
Collaborate with the broader Cybersecurity team to develop and refine playbooks, runbooks, and response procedures.
Compliance and Risk Management
Ensure cloud environments comply with applicable regulatory frameworks and standards, including NIST CSF, CIS Benchmarks, SOC 2, and PCI DSS.
Conduct risk assessments and vulnerability analyses for cloud\-hosted systems and AI workloads.
Partner with IT, DevOps, and application teams to integrate security into the software development lifecycle and cloud deployment pipelines (DevSecOps).
Maintain documentation of security configurations, controls, and audit evidence.
Collaboration and Continuous Improvement
Work cross\-functionally with infrastructure, application development, and operations teams to embed security into cloud initiatives from inception.
Stay current on emerging cloud security threats, AI security risks, and industry developments, including OWASP LLM Top 10 and MITRE ATLAS frameworks.
Contribute to security awareness efforts and provide guidance to internal teams on cloud security best practices.
Requirements
Bachelor's degree in Computer Science, Information Technology, Cybersecurity, or a related field, or equivalent practical experience.
3 to 5 years of experience in cloud security engineering or a closely related cybersecurity discipline.
Hands\-on experience securing cloud environments in AWS, with working knowledge of Azure.
Proficiency with cloud security tools and services such as AWS Security Hub, AWS GuardDuty, Microsoft Defender for Cloud, and related platforms.
Knowledge of adversarial machine learning concepts and AI\-specific attack vectors.
Strong understanding of IAM, network security, encryption, and cloud security architecture principles.
Experience with security automation, infrastructure as code (IaC), and DevSecOps practices.
Familiarity with AI and machine learning concepts as they apply to security use cases, including anomaly detection and automated threat response.
Experience with AI security tooling, including tools designed to secure LLM integrations, model endpoints, and AI\-driven workflows.
Knowledge of compliance frameworks including NIST CSF, CIS Benchmarks, SOC 2, and PCI DSS.
Preferred Qualifications
Familiarity with SIEM platforms such as Splunk or Microsoft Sentinel.
Experience with container security and Kubernetes environments.
Relevant certifications such as AWS Certified Security – Specialty, Certified Cloud Security Professional (CCSP), CompTIA Security\+, Microsoft Certified: Security Operations Analyst Associate, or an AI security/governance credential such as ISACA AAISM or AAIA. Equivalent certifications or practical experience may also be considered.
Experience working in a regulated industry environment.
Skills
Strong analytical and problem\-solving skills with a security\-first mindset.
Ability to communicate complex technical concepts clearly to both technical and non\-technical stakeholders.
Self\-motivated with the ability to manage multiple priorities in a fast\-paced environment.
Collaborative team player with a commitment to continuous learning and professional development.
High attention to detail and a proactive approach to identifying and mitigating risk.
Req \#: 70394
Pay Range: Based on qualifications.
Please be advised that the actual salary offered for any position is subject to the company's sole discretion and may be influenced by various factors, including but not limited to the candidate's qualifications, experience, location, and overall fit for the role.
Herc Rentals values its employees and provides excellent compensation and benefits packages which are not limited to the following.
Keeping you healthy
Medical, Dental, and Vision Coverage
Life and disability insurance
Flex spending and health savings accounts
Virtual Health Visits
24 Hour Nurse Line
Healthy Pregnancy Program
Tobacco Cessation Program
Weight Loss Program
Building Your Financial Future
401(k) plan with company match
Employee Stock Purchase Program
Life \& Work Harmony
Paid Time Off (Holidays, Vacations, Sick Days)
Paid parental leave.
Military leave \& support for those in the National Guard and Reserves
Employee Assistance Program (EAP)
Adoption Assistance Reimbursement Program
Tuition Reimbursement Program
Auto \& Home Insurance Discounts
Protecting You \& Your Family
Company Paid Life Insurance
Supplemental Life Insurance
Accidental Death \& Dismemberment Insurance
Company Paid Disability Insurance
Supplemental Disability Insurance
Group Legal Plan
Critical Illness Insurance
Accident Insurance
Herc does not discriminate in employment based on the basis of race, creed, color, religion, sex, age, disability, national origin, marital status, sexual orientation, citizenship status, political affiliation, parental status, military service, or other non\-merit factors.
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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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Herc Rentals, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Herc Rentals AI Hiring
Herc Rentals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bonita Springs, FL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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