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About This Role
Perdue Farms is a fourth\-generation, family\-owned food and agricultural business deeply rooted in tradition yet with a forward\-thinking mindset. We believe that success starts with our people, and our culture is built on a foundation of teamwork, integrity, and respect, where every voice matters and everyone is encouraged to contribute to our shared goals. We are dedicated to creating a supportive, inclusive environment where associates feel valued and inspired to make an impact, both within the company and in the communities we serve. From promoting growth and development to prioritizing work\-life balance, we’re committed to helping our team members thrive. That's Perdue.
Summary
Shape the future of AI security and help protect the next generation of intelligent systems. As the Lead AI Security Operations Engineer, you will lead the design, security, and operation of autonomous and semi\-autonomous AI systems across both Information Technology (IT) and Operational Technology (OT) environments. You will drive AI\-powered security operations by building and managing an advanced Security Operations Center capable of detecting, monitoring, and responding to threats targeting both traditional infrastructure and agentic AI systems. Partnering with cross\-functional teams, you will strengthen the organization's cybersecurity posture through red teaming, vulnerability management, and the development of enterprise security guardrails. This role is ideal for an innovative cybersecurity leader who thrives at the intersection of AI, critical infrastructure, and emerging technologies while making a lasting impact on enterprise security.
The salary range for this position is $128,000 \- $192,000 per year, based on experience and qualifications with annual bonus available (variable depending on performance).
In addition to the base salary, Perdue offers a competitive benefits package, including medical/Rx, 401(k) with employer match after 1 year, critical illness, accident insurance, dental, vision, life insurance, optional group life insurance, short\-term and long\-term disability protection, flexible spending accounts and paid time off.
Principal Essential Duties \& Responsibilities
- Design, build, and operate an AI\-enabled Security Operations Center for Information Technology, Operational Technology, and agentic AI systems.
- Deploy AI\-driven detection, triage, and response capabilities to augment and automate traditional Security Operations Center workflows.
- Integrate and optimize Security Information and Event Management, Security Orchestration, Automation and Response, and AI analytics platforms for intelligent detection and response.
- Conduct threat hunting focused on AI misuse, adversarial attacks, Operational Technology and Industrial Control Systems threats, and behavioral anomalies.
- Build detection mechanisms for anomalous or unsafe agent behavior.
- Monitor agent actions, decisions, and tool usage.
- Ensure Security Operations Center operations meet regulatory logging, monitoring, and incident response requirements.
- Assist with vulnerability management programs, including continuous scanning, risk\-based prioritization, and remediation tracking.
- Integrate vulnerability intelligence into detection and response workflows.
- Partner with engineering teams to ensure timely remediation of critical vulnerabilities.
- Manage enterprise certificate lifecycle management, including issuance, renewal, rotation, and revocation.
- Maintain and secure Public Key Infrastructure and trust chains.
- Implement key management and encryption standards.
- Lead and support incident response activities, including investigation, containment, and remediation.
- Perform root cause analysis and implement preventive controls.
- Develop and maintain security playbooks, including AI\-assisted response workflows.
- Continuously improve detection and response capabilities through automation and AI integration.
Minimum Education and Experience
- 7 years’ experience in Information Security, including securing AI and machine learning systems or automation platforms.
- Experience operating advanced Security Operations Centers or AI\-driven security environments.
- Experience in Information Technology and Operational Technology environments, including Industrial Control Systems and Supervisory Control and Data Acquisition systems.
- Strong expertise in threat detection, incident response, threat hunting, cloud security, identity and access management, and network security.
- Hands\-on experience with Security Information and Event Management, Security Orchestration, Automation and Response, Endpoint Detection and Response, Network Detection and Response technologies, and enterprise log analysis.
- Proven experience with AI red teaming, adversarial testing, or implementing security guardrails.
- Experience in vulnerability management and certificate/Public Key Infrastructure management.
- Knowledge of security frameworks, including NIST Cybersecurity Framework, ISO 27000, and Center for Internet Security frameworks.
- Bachelor’s degree or equivalent experience.
- Deep understanding of AI security risks and mitigation strategies.
- Familiarity with large language models, prompt engineering, and agent architecture.
- Experience with Zero Trust Architecture.
- Relevant certifications, including Certified Information Systems Security Professional, GIAC Certified Intrusion Analyst, GIAC Global Industrial Cyber Security Professional, cloud security certifications, or equivalent experience.
Physical Requirements and Environmental Factors
- Position is mostly sedentary but may require occasional movement to other offices or buildings.
- May need to move light equipment or supplies from one place to another.
- May need to access files, supplies, and equipment.
- Work activity is performed in an office, open\-partitioned, cubicle environment.
*Perdue Farms Inc. is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.*
Salary Context
This $128K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Perdue, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $128K to $192K.
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.
Perdue AI Hiring
Perdue has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Salisbury, MD, US. Compensation range: $192K - $192K.
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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