Senior AI Product Manager (AI Builder)

About this role

About the Role WEX is putting governed, production-grade AI agents to work across Mobility, Payments, and Benefits to turn enterprise data, systems, and deep domain expertise into resolved outcomes at scale. We are seeking a Senior AI Product Manager Builder to join a forward-deployed Builder Pod . Operating shoulder-to-shoulder with an AI Tech Builder and embedded domain experts, you will turn manual, judgment-heavy workflows into governed, agent-assisted experiences. This is not a traditional PM role focused on writing static requirements and passing them over the wall. You are a hands-on builder who moves seamlessly from business workflow mapping to live prototyping, custom evaluations, and production monitoring. You will own the problem, user outcome, business value case, agent behavior, and evaluation strategy, while your Tech Builder partner owns the production engineering architecture and scalability.

What Makes an AI PM Builder Different at WEX: • Build to Learn: Put working concepts directly into users' hands early rather than waiting for perfect requirements.

• Prototype, Not Just Describe: Use AI tools, low-code/no-code platforms, and lightweight code/configurations (Markdown, YAML, JSON) to make product ideas tangible.

• Get Inside the Agent: Inspect prompts, system context, tool calls, traces, outputs, and failure modes instead of treating AI as a black box.

• Write and Run Evals: Define golden datasets, representative edge cases, scoring rubrics, and judge criteria to drive product decisions through evidence.

• Reimagine Workflows: Focus on end-to-end outcomes—determining where AI should reason, retrieve, automate, or route control to a human (and knowing when deterministic software or human judgment is safer).

• Drive Measurable Value: Measure success by concrete business outcomes (cost, cycle time, accuracy, revenue, risk, adoption) rather than output volume.

• Build for Scale and Reuse: Package learnings, prompts, evals, and agent patterns so every build makes the next deployment faster and cheaper.

Strategy, Workflow Mapping & Value Case • Embed directly with operators, customers, and domain experts across Mobility, Payments, and Benefits to map actual current-state workflows, friction, and failure demand.

• Define future-state experiences, set baseline performance metrics (cycle time, manual hours, containment, error rates), and select the smallest high-value slice to test feasibility and trust.

Hands-On Prototyping & Agent Design • Rapidly build and test agent behavior, system context, prompts, tool flows, retrieval/grounding, and multi-agent or human-in-the-loop orchestrations using WEX’s Agentic AI platform.

• Pair directly with your Tech Builder to open repositories, adjust configurations, test scenarios, examine execution traces, and debug agent logic together.

• Distinguish between rapid exploratory learning prototypes and production systems requiring engineering hardening.

Product Evaluation & Behavioral Quality • Treat evaluation as a core product discipline by defining representative, edge-case, and adversarial scenarios alongside domain experts.

• Establish pass/fail criteria, assertions, and scoring rubrics covering accuracy, tool selection, citation quality, policy adherence, hallucination rates, PII/PHI/PCI safety, and cost per outcome.

• Analyze failed traces to build error taxonomies (distinguishing between prompt, context, retrieval, model, or UX issues) to prevent "demo-driven development."

Spec-Driven Development & Forward Deployment • Translate learnings into versioned repository artifacts (PRDs, acceptance criteria, decision logic, eval harnesses) that serve as a shared source of truth.

• Maintain clear traceability: User Problem → Workflow → Requirement → Scenario → Eval → Production Metric .

• Navigate enterprise legacy systems, fragmented APIs, and complex operational policies to show working software in real environments.

Responsible Governance & Value Measurement • Partner with Risk, Compliance, Security, Legal, and AI Governance from day one to embed decision boundaries, entitlements, auditability, prompt-injection defenses, and fallback controls.

• Instrument live product usage to connect agent performance directly to top-line and bottom-line business ROI.

• Package reusable patterns, runbooks, and controls so domain teams can seamlessly operate and extend capabilities as solutions mature.

Required Qualifications: • 7+ years of Product Management experience shipping software, data, platform, automation, or AI products to production at scale.

• Applied AI Expertise: Direct experience with generative AI, LLMs, retrieval/grounding (RAG), tool calling, context management, orchestration, structured outputs, and human-in-the-loop workflows.

• Evaluation Discipline: Proven experience building or running AI evaluations using golden datasets, quality rubrics, failure analysis, or model-based judges.

• Prototyping & Technical Literacy: Demonstrated ability to use AI-assisted dev tools, low-code frameworks, APIs, and version-controlled configuration files (Markdown, JSON, YAML) to bring concepts to life.

• Systems & Tradeoff Thinking: Skill in balancing accuracy, autonomy, latency, token/system cost, security, and user experience, with a clear understanding of when to use probabilistic AI versus deterministic code.

• Communication & Collaboration: Track record of driving alignment across cross-functional partners (Engineers, Risk, Legal, Operations) and making complex AI tradeoffs clear to executive audiences.

• Education: Bachelor’s degree in a related field or equivalent practical experience.

Preferred Qualifications: • Experience in highly regulated industries such as Fintech, Payments, Health/Benefits, Fleet, or Financial Services.

• Background working with enterprise AI governance frameworks, model risk management, responsible AI, or auditability.

• Hands-on experience with agent frameworks, prompt/context engineering

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