Principal Digital Product Manager, Applied AI

Caterpillar Inc.•Peoria, IL

1d•$159,120 - $258,570•Onsite

About The Position

The AI Accelerator is a key enabler of Caterpillar’s next chapter — designed to enable and scale AI adoption and progress across the enterprise. Its mission is to identify opportunities, run experiments, accelerate adoption, ensure AI value realization, support strategic partner engagement, and help develop the frameworks to scale AI solutions globally. We are building a team of experienced and visionary Applied AI Product Managers to partner directly with our business and functional teams, helping them shape and execute their AI roadmaps and bring high-value AI capabilities to life. This unique role sits at the intersection of business strategy, technical AI/ML capabilities, and user experience. The ideal candidate will have a deep understanding of AI principles, a passion for solving user problems, and a proven ability to guide technical teams in bringing complex AI capabilities from the lab to production.

Requirements

Strategic Vision: Ability to look beyond current constraints and help a business team see and articulate where AI can take them.
Builder: Comfort getting into the weeds — spec driven development for quick prototypes, testing prompts, and standing up working demos rather than only writing specs.
Data-Driven Decision Making: Relies heavily on metrics, data, and experimentation to guide product prioritization.
Bias for Action: Proactive and results-oriented, with a track record of successfully shipping complex products.
Cross-functional Collaboration: A natural collaborator who can build consensus and drive outcomes across diverse engineering, data science, and business teams.
Extensive professional experience (typically 5+ years), with proven experience as a Product Manager focused on Agentic AI, Generative AI, Machine Learning, or AI-powered products (typically 3+ years).
A solid understanding of the AI lifecycle, common model types (e.g., NLP, Computer Vision, Predictive Models, LLMs), feature engineering, and the challenges of deploying and monitoring models in production environments (MLOps). Don’t need to be an expert, but familiarity with tradeoffs and problem types i.e. predictive modeling vs. agentic AI will be important.
Demonstrated ability to personally build functional prototypes using modern AI tooling (spec driven development and/or low code tools) — a forward-deployed sensibility that pairs product judgment with the ability to build.
Exceptional written and verbal communication skills, with the ability to articulate complex AI concepts to non-technical business leaders and users, as well as define detailed requirements for technical teams.
Proven ability to define and execute a product roadmap based on quantitative data and user research.

Nice To Haves

Direct experience or degree in Data Science or Engineering
Experience with cloud-based ML platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML)
Prior experience managing products in a specific industry (e.g., FinTech, Healthcare, E-commerce)
Familiarity with A/B testing frameworks for evaluating and improving model-driven features

Responsibilities

Shape the Business Team’s AI Roadmap: Embed with assigned business functions to help them define their AI vision, priorities, and roadmap. Identify high-impact opportunities where applied AI can deliver real business value for that team — you’re enabling their roadmap, not running a central product line.
Translate Insights to Requirements: Work closely with stakeholders, business analysts, and users to understand needs. Translate these needs into clear, concise Epics, User Stories, and PRDs (Product Requirements Documents) that specifically address the unique constraints and capabilities of AI/ML systems.
Build to Learn (Forward-Deployed Mindset): Rapidly prototype AI solutions alongside business users — building quick, functional proofs-of-concept with leading AI tools (e.g., Claude, Copilot, low-code agent builders) to validate value before committing to full production builds.
Lead the AI Development Lifecycle: Collaborate daily with Data Scientists, ML Engineers, and Software Engineers to manage the AI prototype development lifecycle, including drafting PRDs, technical discovery, agentic workflow design/development and evaluating success.
Transfer Capability: Build frameworks and playbooks that business teams can use to more independently identify, define and build prototypes. Progressively shift ownership to the business.
AI Governance and Compliance: Advocate for and ensure that AI products are developed according to Caterpillar’s AI governance and compliance requirements.