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About This Role
Cargill is a family company committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. We sit at the heart of the supply chain, partnering with producers and customers to source, make and deliver products that are vital for living. By providing customers with life’s essentials, we enable businesses to grow, communities to prosper, and consumers to live well.
This position is in our Ag \& Trading enterprise, where we connect producers and users of grains and oilseeds around the globe through origination, trading, processing, and distribution. We also offer a range of farmer services and risk management solutions.
Job Purpose and Impact
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The Smart Manufacturing \& Supply Chain AI Enablement Lead is accountable for executing AI\-enabled product development from concept through deployment, adoption, scaling, and continuous improvement to deliver measurable value across Manufacturing \& Supply Chain.
This role partners with Operations, Supply Chain, and technology teams to shape and deliver an AI product pipeline that includes Gen AI, agentic AI, machine learning, automation, analytics, and workflow orchestration, while ensuring alignment to strategic objectives, responsible AI standards, adoption, and value realization.
Key Accountabilities
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- Lead end\-to\-end AI product execution, including discovery, business case development, solution design, prioritization, pilots, deployment, scaling, and value tracking.
- Translate business needs into AI product vision, roadmaps, prioritized backlogs, and delivery plans that balance near\-term execution with long\-term capability development.
- Partner with senior business and enterprise stakeholders to shape priorities, resolve trade‑offs, and align investments, defining KPIs and value realization measures to guide decision‑making across the portfolio.
- Drive development and scaling of Smart Manufacturing \& Supply Chain AI products, including Gen AI assistants, agentic workflows, intelligent automation, predictive analytics, and decision\-support tools.
- Translate business problems into AI product requirements, including user workflows, data needs, guardrails, testing criteria, adoption plans, and scalable operating models.
- Establish clear deliverables, project charters, delivery plans, and reports to plan, execute, and document AI product initiatives.
- Drive readiness, adoption, and sustained value realization in partnership with change management and operational teams.
- Establish operating rhythms for intake, prioritization, stage\-gate decisions, backlog reviews, demos, adoption tracking, and value realization.
- Apply AI capabilities to improve manufacturing performance, decision quality, process efficiency, knowledge management, and frontline workflows.
- Identify risks, dependencies, and constraints early, proactively recommending mitigation strategies and trade‑off decisions to maintain momentum and outcomes.
- Act as a functional thought leader on product management practices and emerging digital, analytics, automation, Gen AI, and agentic AI technologies.
- Perform other duties as assigned.
Qualifications
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### Minimum Qualifications
- Bachelor’s degree in a related field or equivalent experience
- 4–6 years of relevant experience in product management, product ownership, digital delivery, or related roles
- Previous experience working in manufacturing and/or supply chain environments
- Experience translating business requirements into prioritized product backlogs and deliverables
- Technical fluency with data, analytics, APIs, Python, automation, or AI development environments to execute and partner effectively with technical teams.
- Experience developing or deploying AI\-enabled solutions such as Gen AI assistants, agentic workflows, intelligent automation, predictive analytics, or decision\-support tools.
### Preferred Qualifications
- Demonstrated ability to lead through influence, aligning diverse stakeholders around a common product vision and driving outcomes
- Experience managing digital, data, analytics, automation, or AI products applied to operational environments
- Strong analytical skills with experience using data to inform prioritization and decision‑making
- Working knowledge of agile values, frameworks, and metrics
- Experience supporting technology‑enabled business transformation or change initiatives
- Understanding of AI product development concepts, including Gen AI, agentic AI, machine learning, automation, data products, or digital workflow solutions.
- Familiarity with Gen AI and agentic AI solution patterns, including prompt design, retrieval\-augmented generation, workflow orchestration, tool/action integration, model evaluation, human\-in\-the\-loop controls, and responsible AI practices.
At Cargill, we are committed to building a workplace where individuals from all backgrounds feel respected, valued, and able to contribute fully. We believe that bringing together different perspectives strengthens our teams and drives better outcomes.
Cargill is an equal opportunity employer and is committed to providing an accessible recruitment experience. If you require accommodation at any stage of the hiring process, please let us know so we can work with you to meet your needs.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cargill, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cargill AI Hiring
Cargill has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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