Senior Technical Product Manager - AI Platform

Location:
Remote, with a requirement to reside within 30 miles of one of the following locations: Portland, ME; Boston, MA; Chicago, IL; Washington, DC; Dallas, TX; or San Jose, CA.

About WEX AI Team

The WEX AI team is building the next generation of intelligent systems that power core financial and business operations. We operate at the intersection of applied machine learning (ML) and artificial intelligence (AI), cloud infrastructure, and enterprise platform strategy—developing robust platforms that support a wide range of AI use cases, from predictive analytics to generative AI applications.

Our mission is to industrialize AI across WEX by delivering a unified platform that makes model development, deployment, and monitoring scalable, efficient, and reliable.

About the Role

We are seeking a Senior Technical Product Manager - AI Platform to lead product platform strategy for applied AI systems, MLOps, and model deployment infrastructure. As a senior individual contributor, you will own the vision, roadmap, and execution of tools and systems that support the entire AI model lifecycle—spanning training, versioning, deployment, observability, and retraining, and implementation of AI governance principles and rules at a platform level.

You’ll work closely with data scientists, AI/ML engineers, platform architects, DevOps, and product engineering teams to build infrastructure that enables rapid, trustworthy, and scalable AI productization across the company.

You will also define and track the impact and performance of the AI platform, establishing metrics to assess value delivered, ROI, and platform effectiveness across use cases and teams.

How You'll Make an Impact

AI Platform Strategy & Architecture

• Define and evolve the product roadmap for the AI/ML platform, including model training infrastructure, CI/CD pipelines for ML, and production deployment frameworks.

• Develop strategies for democratizing access to reusable AI capabilities across teams through modular APIs, tooling, and services.

• Evaluate trade-offs between centralization and decentralization of ML workflows based on organizational maturity, scalability, and compliance needs.

• Collaborate with technical leaders to identify opportunities to adopt or extend open-source and cloud-native tools for model lifecycle management.

• Define and support platform architecture and platform-level guidelines; ensure each product’s solution architecture aligns with overarching platform strategy.
   

End-to-End MLOps & AI Infrastructure

• Partner with MLOps and platform engineering teams to deliver scalable model deployment pipelines, monitoring systems, and rollback mechanisms.

• Define product requirements for model testing environments, A/B experimentation frameworks, and automated retraining triggers.

• Advocate for tooling that enables visibility into model performance, drift, data health, and cost efficiency in production.

• Support the development of secure, policy-aligned mechanisms for managing model artifacts, features, and associated metadata.
   

Model Lifecycle & Developer Enablement

• Work closely with data scientists and ML engineers to understand their workflows, pain points, and platform needs—from ideation and experimentation through to production.

• Define APIs, user interfaces, and platform abstractions that reduce friction and accelerate experimentation velocity while enforcing best practices.

• Champion developer experience by ensuring platform components are discoverable, reliable, and well-documented.

• Enable model reproducibility and lineage tracking through structured versioning, data contract enforcement, and audit-ready practices.
   

Cross-Functional Execution & Impact Tracking

• Serve as the product lead in cross-disciplinary squads focused on AI deployment, model reliability, and applied use case acceleration.

• Collaborate with cloud infrastructure, data platform, and compliance teams to ensure secure, cost-effective, and scalable platform growth.

• Translate technical platform investments into business-level KPIs, including model time-to-market, uptime, and customer-facing value.

• Facilitate alignment between experimentation, infrastructure, and AI product delivery roadmaps.

Experience You'll Bring