Senior AI Product Manager (AI Builder)

WEX•Seattle, WA

About The Position

WEX is seeking a Senior AI Product Manager Builder to join a forward-deployed Builder Pod. This role involves operating with an AI Tech Builder and domain experts to transform manual workflows into governed, agent-assisted experiences. It's a hands-on position requiring seamless transition between business workflow mapping, live prototyping, custom evaluations, and production monitoring. The AI PM Builder will own the problem, user outcome, business value case, agent behavior, and evaluation strategy, while the Tech Builder partner handles production engineering architecture and scalability. Key aspects of the role include building to learn, prototyping rather than just describing, understanding agent internals, writing and running evaluations, reimagining workflows for AI integration, driving measurable business value, and building for scale and reuse.

Requirements

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

Nice To Haves

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 tools, or AI experimentation platforms.

Responsibilities

Embed directly with operators, customers, and domain experts to map current-state workflows, friction, and failure demand.
Define future-state experiences, set baseline performance metrics, and select high-value slices for testing feasibility and trust.
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.
Distinguish between rapid exploratory learning prototypes and production systems requiring engineering hardening.
Define representative, edge-case, and adversarial scenarios for evaluation 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 and prevent "demo-driven development."
Translate learnings into versioned repository artifacts (PRDs, acceptance criteria, decision logic, eval harnesses) that serve as a shared source of truth.
Maintain clear traceability from User Problem to Production Metric.
Navigate enterprise legacy systems, fragmented APIs, and complex operational policies to show working software in real environments.
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.