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About This Role
Description
VP of Sales & Supply Chain
Purpose and Scope/General Summary: Mantiqueira is seeking a Vice President of Sales & Supply Chain to lead our strategic entry into the egg industry. Based in Phoenix, AZ, this executive role will oversee and optimize the entire supply chain network while driving sales growth, operational excellence, and market competitiveness for this exciting new venture.
Responsibilities:
- Lead and optimize the end-to-end supply chain network, including multiple production and distribution centers, ensuring efficient, cost-effective, and compliant operations.
- Develop and execute comprehensive supply chain strategies that drive operational excellence, maximize resource utilization, and support business transformation.
- Oversee demand planning, sourcing, vendor management, logistics, inventory management, and order fulfillment, implementing best-in-class technologies and methodologies (e.g., ERP, MRP, DRP, S&OP, Lean, Six Sigma).
- Foster strong partnerships with customers, suppliers, and internal stakeholders to enhance collaboration and drive profitable growth.
- Set, monitor, and achieve key performance indicators (KPIs) for forecast accuracy, capacity utilization, schedule adherence, inventory turns, on-time delivery, and cost management.
- Lead and develop high-performing sales and supply chain teams, providing coaching, talent development, and strategic direction.
- Build and maintain top-to-top relationships with major customers and distributors, driving joint business planning and incremental sales growth.
- Collaborate cross-functionally with Marketing, Product Development, Finance, and Operations to align strategic business plans and maximize channel profitability.
- Manage contract negotiations, RFPs, business development initiatives, and new product introductions, ensuring alignment with market trends and customer needs.
- Regularly review and update strategies with executive leadership to adapt to industry changes and market dynamics.
- Other duties as assigned
Qualifications:
- Bachelor’s degree required; MBA preferred.
- 15+ years of progressive supply chain management experience, including senior leadership roles in the food industry; direct experience in the egg industry highly preferred.
- 15+ years of sales leadership experience, with a proven track record of building and leading high-performance teams.
- Deep expertise in supply chain processes, methodologies, and technologies (ERP, JIT, S&OP, Lean, Six Sigma).
- Strong analytical, financial, and P&L management skills; demonstrated ability to manage category and customer profitability.
- Professional certifications in Operations, Purchasing, or Logistics are a plus.
- Exceptional communication and presentation skills, with experience engaging senior leadership and external partners.
- Proven success in strategic planning, negotiation, and driving projects to completion.
- Ability to leverage industry data and insights to deliver value-added solutions aligned with customer and channel needs.
- Demonstrated experience supporting new product and customer introductions across all supply chain functions.
- Can perform the functions of the job with or without a reasonable accommodation
- As a salaried position with the company, you may be required to travel at some point to other facilities, to attend Company events, or as a representative of the Company in other situations. Unless otherwise specified in this posting, the amount of travel may vary and the most qualified candidate must be willing and able to travel as business needs dictate.
The Company is dedicated to ensuring a safe and secure environment for our team members and visitors. To assist in achieving that goal, we conduct drug, alcohol, and background checks for all new team members post-offer and prior to the start of employment. The Immigration Reform and Control Act requires that verification of employment eligibility be documented for all new employees by the end of the third day of work.
About us: JBS USA is a leading global food company providing diversified, high-quality products to customers in approximately 100 countries on six continents. Our team members and facilities in the United States allow us to offer a diverse portfolio of fresh, value added and branded beef, pork, chicken and prepared foods products. JBS USA is also the majority shareholder of Pilgrim’s, the largest poultry company in the world. JBS USA employs more than 72,000 team members in 31 United States and Canada. Our corporate office is located in beautiful Greeley, Colorado, where our 1,200 team members onsite enjoy more than 300 days of sunshine a year.
Our mission: To be the best in all that we do, completely focused on our business, ensuring the best products and services to our customers, a relationship of trust with our suppliers, profitability for our shareholders and the opportunity of a better future for all of our team members.
Our core values are: Availability, Determination, Discipline, Humility, Ownership, Simplicity, Sincerity
EOE, including disability/vets
Unsolicited Assistance: JBS and its companies do not accept unsolicited assistance from any recruitment vendors for any of our open jobs. All resumes or candidate profiles submitted by recruitment vendors or headhunters to any employee at JBS and its companies or via the applicant tracking system, in any form without a valid written request and search agreement previously approved by HR, will be solely owned by JBS and its companies. No fees will be paid should the candidate be hired by JBS and its companies because of an unsolicited referral.
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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At JBS Foods, 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 $154,000 based on 8,743 positions with disclosed compensation.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
JBS Foods AI Hiring
JBS Foods has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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