Senior/Principal Product Manager - Machine Learning and AI

About the role

Our Machine Learning and Generative AI Platform teams are at the forefront of Wise's AI transformation. We're building the foundations that enable our entire organisation to harness the power of AI safely and effectively. Our ML Platform provides cutting-edge tools that turn data science ideas into production with minimal effort, while our GenAI Platform empowers all Wisers to leverage state-of-the-art generative AI through seamless integration, robust governance, and best-in-class developer experience.

We're looking for a Technical Product Manager who can get their hands dirty. This isn't a role where you'll just write requirements - you'll prototype solutions, analyze complex datasets, and work shoulder-to-shoulder with our engineering teams to shape the future of AI at Wise. You'll navigate the rapidly evolving GenAI landscape while ensuring we move fast without compromising on security, privacy, or compliance.

This is a unique opportunity to drive AI adoption across a global fintech, where your technical depth will be as valuable as your product sense.

How we work

We work differently and we’re proud of it. Our teams are empowered to solve the most urgent and relevant problems they see for our customers. We all share the responsibility of making Wise a success. We empower Wisers to make decisions and take ownership of how they work best. Teams and individuals have different needs – that’s why we have company-wide principles, and then our teams set their own guidelines.

What will you be working on

Ship the AI platform that unlocks innovation:

• Drive adoption of our ML/GenAI infrastructure by identifying friction points through data analysis and shipping solutions that reduce time-to-production from weeks to days

• Build and validate technical roadmaps using prototypes, SQL analytics, and hands-on experimentation with our stack (Sagemaker, MLflow, Ray, Bedrock)

• Define success metrics and implement dashboards that track everything from model performance to business impact

Balance speed with safety:

• Design governance frameworks that enable rapid experimentation while ensuring compliance - automating risk assessments and privacy checks

• Partner with security to implement model monitoring and access controls that protect customer data without blocking innovation

• Create cost optimization strategies backed by data, reducing ML infrastructure spend while scaling usage

Drive strategic technical decisions:

• Evaluate and select AI vendors through hands-on technical assessment and ROI analysis

• Work with engineering to define architecture that scales - from feature stores to multi-cloud inference

• Enable 10x more teams to use AI by building self-service tools, clear documentation, and reusable components