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About This Role
There are over 7 billion people on this planet. And by 2050, there will be 2 billion more... many moving into urban centers at an unprecedented rate. Making sure there is enough food, fiber and infrastructure for our rapidly growing world is what we're all about at John Deere. And it's why we're investing in our people and our technology like never before! Here the world's brightest minds are tackling the world's biggest challenges. If you believe one person can make the world a better place, we'll put you to work. RIGHT NOW.
John Deere is an equal opportunity employer, including disabled & veterans.
Primary Location: United States (US) - Iowa - Johnston, IA
Function: Data and Analytics (CA)
Title: AI Analytics Product Engineer - 124876
Onsite/Remote:**Onsite Position
Your Responsibilities
As a AI Analytics Product Engineer for Technology Commercialization/Customer & Commercial Intelligence located in Johnston, IA, (A successful applicant would be expected to work onsite two days per week. Experienced candidates outside of the Des Moines area may be able to substitute quarterly onsite weeks in lieu of this.) you will:
- Design, prototype, and implement user-facing, AI-enabled BI solutions for technology marketing intelligence.
- Utilize the latest AI tools to accelerate BI and data analytics solution development.
- Contribute to internal AI and ML Ops frameworks that accelerate solution development.
- Translate Technology Commercialization stakeholder needs into impactful AI-enabled analytics tools that enable strategic decision-making, tactical marketing, and dealer channel sales/marketing intelligence.
- Develop context and data models to enable BI solutions.
- Utilize Agile methodology to plan and organize tasks.
- Present findings and product demos to stakeholders and executive leaders.
- Collaborate across cross-functional teams including Customer & Commercial Intelligence, Data Products, Technology Marketing, Frontier AI, and Channel AI.
- Propose innovative ways to solve problems by using the latest technology on available enterprise data as well as customer third party data and information.
- Works across regions, platforms and several units within a division.
- Projects are larger, have longer lead time and require involvement with 3 - 5 areas of support.
- Works independently and may serve as a mentor and reviewer of other team members’ output.
- May lead a small project team and oversee early-talent team members.
VISA Sponsorship is NOT available for this position
What Skills You Need
- Proficiency in SQL, Python, PySpark, Databricks, Databricks Apps, and Git.
- Proficiency in cloud platforms such as AWS or Azure.
- Proficiency in data visualization tools and techniques.
- Proficiency in agentic AI coding tools such as Codex, Claude Code, GitHub Copilot.
- Proficiency with OpenAI and/or Anthropic models.
- Proficiency in Agile methodology, operating processes, and tools.
- Experience with developing MCP servers, AI tools, AI skills, and with context engineering.
- Experience in reporting tools such as PowerBI or Tableau.
- Experience with Stakeholder management, requirements gathering, and data storytelling.
What Makes You Stand Out
- Experience developing and deploying AI solutions.
- Experience coordinating large projects across cross-functional teams.
- Experience developing and deploying self-serve BI solutions.
- Experience in sales and/or marketing.
- Experience in agriculture and/or agronomy.
- Certifications in cloud platforms such as AWS or Azure.
- Experience in product management or related roles.
Education
Ideally you will have a degree or equivalent related work experience in the following:
- Bachelor's Degree. Master's or Ph.D. preferred
What You'll Get
At John Deere, you are empowered to create a career that will take you to where you want to go while working in an inclusive team environment. Here, you'll enjoy the freedom to explore new projects, the support to think outside the box and the advanced tools and technology that foster innovation and achievement. Additionally, we offer a comprehensive reward package to help you get started on your new career path, including:
- Flexible work arrangements
- Highly competitive base pay
- Savings & Retirement benefits (401K and Defined Contribution)
- Healthcare benefits with a generous company contribution in the Health Savings Account
- Adoption assistance
- Employee Assistance Programs
- Tuition assistance
- Fitness subsidies and on-site gyms at specific Deere locations
- Charitable contribution match
- Employee Purchase Plan & numerous discount programs for personal use
- Vacation and Holiday Pay
$123,804.00 - $185,700.00 + Benefits
Follow this link to learn more about our Total Rewards Package https://bit.ly/3XCd8fL
Must be 18 years of age or older to apply
The information contained herein is not intended to be an exhaustive list of all responsibilities and qualifications required of individuals performing the job. The qualifications detailed in this job description are not considered the minimum requirements necessary to perform the job, but rather as guidelines.
The terms of the applicable benefit plans, and all company actions administering or interpreting these plans, continue to control. Deere & Company reserves the right to suspend, amend, modify, or terminate the Plan(s) in any manner at any time, including the right to modify or eliminate any cost-sharing between the company and participants. Changes, which can be made at any time, are made by action of the company's board of directors, or to the extent authorized by resolution of its board of directors, or by the Deere & Company Compensation Committee. In the event of a conflict between the language of the official Plan Documents and this document, the language of the official Plan Documents will control.
ACA Section 1557 Nondiscrimination Notice
The John Deere Health Benefit Plans for Salaried Employees and The John Deere Benefit Plan for Wage Employees comply with applicable Federal civil rights laws and do not discriminate on the basis of race, religion, color, national origin, sex, age, sexual orientation, gender identity or expression, status as a protected veteran, or status as a qualified individual with disability.
Salary Context
This $123K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At John Deere, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($154K) sits 27% below the category median. Disclosed range: $123K to $185K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
John Deere AI Hiring
John Deere has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Moline, IL, Johnston, IA, US, Santa Clara, CA. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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