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About This Role
Rockwell Automation is a global technology leader focused on helping the world’s manufacturers be more productive, sustainable, and agile. With more than 28,000 employees who make the world better every day, we know we have something special. Behind our customers - amazing companies that help feed the world, provide life-saving medicine on a global scale, and focus on clean water and green mobility - our people are energized problem solvers that take pride in how the work we do changes the world for the better.
We welcome all makers, forward thinkers, and problem solvers who are looking for a place to do their best work. And if that’s you we would love to have you join us!
Job Description
The AI Product Owner serves as the primary business liaison between GSM (Global Sales and Marketing) functions and the GSM AI delivery team. You will identify business challenges and lead discovery. You will gather and clarify requirements, prioritize AI opportunities, and partner with experts. These experts include AI Developers, Enablement Specialists, Process Architects, and Project Managers. Together, you will deliver solutions that improve productivity, efficiency, and user experience.
You will translate needs into actionable work, own use cases through discovery and delivery, maintain priorities, coordinate stakeholder decisions, and confirm that solutions achieve intended outcomes. The AI Product Owner is not accountable for enterprise AI strategy or portfolio governance. Instead, you will drive execution of the GSM AI roadmap through business partnership, disciplined product ownership, and use-case leadership.
Your Responsibilities:
Discovery & Problem Definition
- Partner with GSM stakeholders to identify business challenges, users, workflows, pain points, and desired outcomes.
- Define current and future-state processes, scope, constraints, dependencies, and important partners.
- Focus on the underlying business need rather than a predetermined solution.
Requirements & Backlog Management
- Gather, validate, and document requirements, user stories, use cases, and acceptance criteria.
- Manage and maintain the backlog, including enhancements, defects, priorities, dependencies, and decisions.
- Ensure work is clearly defined and development ready before execution.
Prioritization & Governance
- Assess requests based on business value, user impact, complexity, risk, readiness, and capacity.
- Recommend priorities, tradeoffs, and sequencing to GSM AI leadership.
- Escalate unresolved ownership, policy, funding, data, or capacity issues when needed.
Solution Delivery
- Partner with Process Architects and AI Developers to translate business needs into effective solutions.
- Support backlog refinement, design reviews, testing, demonstrations, and acceptance activities.
- Manage scope changes and ensure partners understand impacts and dependencies.
Pilot & Adoption
- Define pilot objectives, success criteria, participants, and feedback mechanisms.
- Collect and translate user feedback into prioritized improvements and adoption actions.
- Partner with enablement and business leaders to support communications, training, and change management.
Value Realization
- Establish success measures and expected outcomes upfront.
- Monitor adoption, usability, efficiency, and business impact metrics.
- Identify opportunities for continuous improvement, reuse, and broader AI adoption.
Stakeholder Communication
- Provide transparent updates on scope, priorities, decisions, risks, and outcomes.
- Maintain alignment between business needs, delivered capabilities, user feedback, and measured results.
Ensure work is paused or closed when ownership, readiness, value, or priority is insufficient. *
The Essentials - You Will Have:
- Bachelor's Degree in Relevant Field
- Legal authorization to work in the U.S. We will not sponsor individuals for employment visas, now or in the future, for this job opening.
The Preferred - You Might Also Have:
- Typically requires 8 years of experience in Product Ownership, Business Analysis, Process Improvement, Commercial Excellence, Digital Transformation, or a related field.
- Experience gathering requirements, facilitating stakeholder discussions, and translating business needs into solutions.
- Proven ability to manage backlogs, priorities, cross-functional initiatives, and competing demands in an evolving environment.
- Experience with AI, automation, digital products, low-code platforms, or enterprise technology solutions.
- Familiarity with Microsoft Copilot, Power Platform, Copilot Studio, Salesforce, or similar platforms.
- Agile, Scrum, Product Owner, or Business Analysis experience and methodologies.
- Experience supporting user testing, pilots, adoption, and change management initiatives.
- Ability to define success metrics and measure adoption, productivity, process improvements, and business outcomes.
- Relevant Agile, Scrum, Product Owner, Business Analysis, or process improvement certification preferred.
What We Offer:
- Health Insurance including Medical, Dental and Vision
- 401k
- Paid Time off
- Parental and Caregiver Leave
- Flexible Work Schedule where you will work with your manager to enjoy a work schedule that can be flexible with your personal life.
- To learn more about our benefits package, please visit at www. raquickfind.com .
At Rockwell Automation we are dedicated to building a diverse, inclusive and authentic workplace, so if you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right person for this or other roles.
This position is part of a job family. Experience will be the determining factor for position level and compensation.
#LI-Hybrid
#LI-DM1
We are an Equal Opportunity Employer including disability and veterans.
If you are an individual with a disability and you need assistance or a reasonable accommodation during the application process, please contact our services team at +1 (844) 404-7247.
Rockwell Automation’s hybrid policy aligns that employees are expected to work at a Rockwell location at least Mondays, Tuesdays, and Thursdays unless they have a business obligation out of the office.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Rockwell Automation, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Rockwell Automation AI Hiring
Rockwell Automation has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Milwaukee, WI, US, Austin, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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