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About This Role
JOB DESCRIPTION
URUS is seeking a strategic and innovative Artificial Intelligence (AI) Product Owner to lead our corporate artificial intelligence transformation. This role will be responsible for co-developing and executing our enterprise AI strategy, fostering an AI-first culture across the organization, identifying high-value use cases for generative AI and agentic systems, and establishing frameworks to measure AI effectiveness and business impact. The ideal candidate combines deep AI/ML expertise with business acumen, change management skills, and the ability to translate emerging AI capabilities into tangible business value.
Reporting to the SVP of Integrated Digital Solutions, the AI Product Owner plays a pivotal role in shaping the digital future of Urus and enabling the technological capabilities required to support sustainable growth and strategic execution.
Key Responsibilities
*AI Strategy Development & Execution (30%)*
- Develop a comprehensive corporate AI strategy aligned with business objectives, including 1-3 year roadmaps for AI adoption across all business functions
- Define AI governance frameworks including ethical AI principles, responsible AI guidelines, data privacy standards, and risk management protocols
- Establish AI investment priorities by evaluating emerging AI technologies (generative AI, agentic systems, computer vision, NLP, etc.) against business needs and ROI potential
- Create and maintain AI technology stack recommendations, including build vs. buy vs. partner decisions for AI platforms and tools
- Partner with executive leadership to secure funding, resources, and organizational commitment for AI initiatives
- Monitor AI industry trends and competitive landscape to ensure the organization remains at the forefront of AI innovation
- Develop AI maturity models and assess organizational readiness for AI adoption across different functions
*AI-First Culture & Change Management (25%)*
- Lead organizational change to build an AI-first mindset across all departments and levels of the organization
- Design and deliver AI education programs including executive briefings, department-specific training, and hands-on workshops
- Establish AI Centers of Excellence or communities of practice to share knowledge, best practices, and success stories
- Create internal AI evangelism programs including champions networks, lunch-and-learns, and innovation challenges
- Develop change management strategies to address resistance, fear, and misconceptions about AI adoption
- Build cross-functional AI literacy by creating accessible resources, playbooks, and documentation for non-technical stakeholders
- Foster an experimentation culture by establishing safe-to-fail environments and rapid prototyping processes
- Partner with People and L&D to integrate AI skills into talent development and recruitment strategies
*Use Case Identification & Prioritization (25%)*
- Conduct discovery sessions with business unit leaders to identify pain points, inefficiencies, and opportunities where AI can create value
- Evaluate and prioritize AI use cases using frameworks that balance business impact, technical feasibility, and strategic alignment
- Develop business cases for AI initiatives including cost-benefit analysis, resource requirements, timelines, and success metrics
- Identify opportunities for generative AI applications including content creation, code generation, customer service automation, and knowledge management
- Explore agentic AI applications where autonomous systems can handle complex multi-step workflows, decision-making, and task orchestration
- Map AI opportunities across the value chain from supply chain optimization to customer experience enhancement
- Maintain an AI opportunity backlog with clear prioritization criteria and regular review processes
- Conduct competitive analysis to identify AI capabilities that could provide strategic differentiation
*Product Management & Delivery (15%)*
- Own the AI product roadmap including features, releases, and dependencies across multiple AI initiatives
- Define product requirements for AI solutions in collaboration with technical teams, business stakeholders, and end users
- Manage AI product lifecycle from ideation through MVP, pilot, scaling, and optimization
- Coordinate with data science, R&D, engineering, and Digital/IT teams to ensure successful implementation of AI solutions
- Oversee vendor relationships for third-party AI platforms, tools, and services
- Ensure AI solutions meet quality standards including accuracy, reliability, fairness, transparency, and security
- Manage AI project portfolios balancing quick wins with transformational initiatives
*Measurement & Value Realization (5%)*
- Establish AI performance frameworks with clear KPIs for each AI initiative linked to business outcomes
- Define metrics for AI effectiveness including accuracy metrics, operational efficiency gains, cost savings, revenue impact, and user adoption
- Create dashboards and reporting mechanisms to track AI performance, ROI, and business value across the organization
- Conduct post-implementation reviews to capture lessons learned and refine future AI initiatives
- Measure AI-first culture adoption through surveys, usage analytics, and behavioral indicators
- Calculate total cost of ownership (TCO) for AI initiatives including infrastructure, licensing, talent, and maintenance costs
- Develop value attribution models to accurately connect AI capabilities to business outcomes
- Establish continuous improvement processes based on performance data and user feedback
- Report on AI progress to executive leadership with clear business impact narratives
Required Qualifications
*Education*
- Bachelor's degree in Computer Science, Data Science, Business, Engineering, or related field
- MBA, Master's in Data Science, AI, or related advanced degree strongly preferred
*Experience*
- 5+ years of progressive experience in product management, AI/ML implementation, or digital transformation roles
- 3+ years of hands-on experience with AI/ML technologies including generative AI (GPT, Claude, Gemini, etc.) and agentic systems
- Proven track record of developing and executing AI strategies in enterprise environments
- Demonstrated success leading matrixed, cross-functional teams and managing complex technology initiatives
- Experience building AI products from concept through production deployment
- Experience with change management and organizational transformation in technology adoption
*Technical Knowledge*
- Deep understanding of AI/ML concepts including supervised/unsupervised learning, natural language processing, computer vision, and reinforcement learning
- Expertise in generative AI including large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and fine-tuning approaches
- Knowledge of agentic AI systems including autonomous agents, multi-agent orchestration, tool use, and reasoning capabilities
- Familiarity with AI platforms and tools such as OpenAI, Anthropic Claude, Azure AI, Google Vertex AI, AWS Bedrock, and open-source frameworks
- Understanding of MLOps practices including model deployment, monitoring, versioning, and governance
- Understanding of data architecture and engineering principles that enable AI applications
- Awareness of AI ethics, bias, and fairness considerations in model development and deployment
- Knowledge of AI security and privacy requirements including data protection and adversarial attack mitigation
*Business & Leadership Skills*
- Strategic thinking with ability to connect AI capabilities to business value and competitive advantage
- Strong business acumen with experience developing ROI models and business cases for technology investments
- Exceptional communication skills with ability to explain complex AI concepts to non-technical audiences
- Stakeholder management expertise including experience influencing senior executives and board members
- Change leadership capabilities with demonstrated ability to drive cultural transformation
- Data-driven decision making with strong analytical and problem-solving skills
- Product management expertise including agile methodologies, user story development, and prioritization frameworks
- Project and portfolio management skills with ability to manage multiple concurrent initiatives
Preferred Qualifications
- Certification in AI/ML such as Google Cloud ML Engineer, AWS ML Specialty, or similar credentials
- Product management certifications such as Certified Scrum Product Owner (CSPO) or Pragmatic Institute
- Knowledge of agricultural or bovine industry applications would be an asset
- Experience with AI governance frameworks and responsible AI practices
- Experience with AI prompt engineering and building effective prompts for various use cases
- Understanding of AI model evaluation techniques and quality assurance processes
- Experience with AI-powered automation tools like Microsoft Power Platform, UiPath, or similar
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 AgSource, 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. Mid-level AI roles across all categories have a median of $147,000.
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.
AgSource AI Hiring
AgSource has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Madison, WI, 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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