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About This Role
Perdue Foods has a goal of becoming the most trusted name in premium proteins by creating products for consumers and for retail and foodservice customers around the globe while changing the way animals are raised for food. It is part of Perdue Farms, 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
Lead the digital and analytics evolution of our live operations. As the Digital Transformation and Data Science Lead of Live Operations, you will design and deliver advanced analytics, AI, and digital solutions that strengthen forecasting, performance, and decision‑making across hatcheries, breeder farms, broiler grow‑out, and feed mills. You will partner with operations, IT, and external technology providers to architect scalable platforms, drive adoption of tools like Dataiku and Power BI, and elevate data‑driven capabilities across the organization. This role is ideal for a strategic, innovative leader who thrives at turning complex challenges into practical, high‑impact digital solutions.
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
- Lead the strategy, design, development, and deployment of advanced analytics, AI, and data science solutions supporting live operations.
- Architect and optimize end‑to‑end data workflows and models using Dataiku or comparable platforms.
- Develop scalable dashboards, reporting frameworks, and visualization strategies in Power BI to support operational and executive decision‑making.
- Develop and support AI and machine learning models for forecasting and operational decision‑making.
- Identify opportunities to enhance performance through data insights, automation, and predictive analytics.
- Establish best practices, standards, and methodologies for analytics development and data governance.
- Lead and influence digital transformation initiatives across live operations, identifying and prioritizing high‑impact opportunities.
- Translate business needs into scalable digital solutions and drive alignment on approach, design, and implementation.
- Drive process improvement through automation, standardization, and advanced data utilization.
- Evaluate emerging technologies and recommend adoption strategies aligned to business objectives.
- Collaborate with operations leaders to identify and prioritize digital opportunities.
- Lead engagement with external vendors and solution providers, influencing design, scope, and delivery of analytics solutions.
- Define project requirements, priorities, and success criteria, ensuring delivery against business objectives.
- Partner with IT to drive data architecture, integration, and platform scalability to support advanced analytics.
Minimum Education and Experience
- Bachelor’s Degree in Data Science, Computer Science, Engineering, Operations Research, or a related field.
- 5\-8 years of experience.
- Strong experience with Dataiku and/or similar data science platforms.
- Advanced proficiency in Power BI or comparable data visualization tools.
- Demonstrated ability to solve complex problems using advanced analytical techniques and independent judgment.
- Experience leading large projects or processes with limited oversight.
- Proven ability to influence cross‑functional stakeholders and translate business needs into technical solutions.
- Strong communication skills with the ability to convey complex concepts to non‑technical audiences.
Preferred Education and Experience
- Experience with SQL and/or Python.
- Knowledge of machine learning techniques and applications.
- Experience in agriculture, manufacturing, or supply chain environments.
- Familiarity with live operations (hatchery, breeder, broiler, or feed mill).
Physical Requirements and Environmental Factors
- Ability to work in office environments, operational facilities, and live operations settings as needed.
- Ability to travel to hatcheries, breeder farms, broiler grow‑out operations, and feed mills.
- Ability to work around operational equipment and within agricultural or production environments.
*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: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 11% below the category median. Disclosed range: $128K to $192K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Perdue AI Hiring
Perdue has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salisbury, MD, US. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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