See how candidates think and approach the work this role demands, before the phone screen. We'll build a video challenge from this posting, and you can edit or share it before it goes live.
Wise is building internal AI-assisted tooling and automated case resolution into its anti-money-laundering operations, a sign that compliance headcount growth is being replaced by automation to meet tightening regulatory review demands.
Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
More about our mission and what we offer.
The team you will join: AML Handling within the AML Squad in the FinCrime Chapter
Why this team exists
Regulatory expectations demand that we review more users, monitor additional typologies, and continuously elevate decision quality each year. Without technical leverage, our AML Investigations unit would keep growing - an expensive route that makes maintaining high quality difficult.
The AML Handling team exists to scale operational growth maintaining high quality. We craft back-office tooling that enables agents to work in a unified space, enhance their daily workflow with Artificial Intelligence, and automatically resolve high-confidence cases.
What will you be working on?
AML Handling team is looking for an experienced Product Engineer to help us build the tools and systems that enable Wise to investigate and resolve financial crime cases effectively. In AML Handling, we improve how operational teams work, using automation and AI to support better decisions, reduce manual effort, and minimise unnecessary friction for customers.
What will you be doing?
Design and build scalable systems for AML case handling and investigation workflows.
Own substantial technical initiatives end to end, from understanding the problem and shaping the technical approach to delivery and operation in production.
Partner with Product, Operations, Data Scientists, and Data Analysts to identify opportunities, shape product decisions, and measure the impact of your work.
Build automation and bring AI capabilities into production, with attention to quality, evaluation, traceability, and appropriate human oversight.
Improve reliability, security, and technical quality through thoughtful system design, testing, code reviews, and knowledge sharing.
The technology you’ll work with
Our primary technology stack is Java, Spring Boot, and Kafka, with SQL databases. You’ll work with distributed services, asynchronous processing, and data at scale.
We design for scalability, observability, privacy, security, reliability, and cost.
What are we looking for?
Strong engineering foundations: 5+ years of experience building backend, distributed, or data-intensive systems, with strong Java skills and experience with Spring, relational databases, and asynchronous messaging.
A strong sense of ownership: You take responsibility for outcomes, navigate ambiguity, and are ready to lead technical initiatives from an initial idea through to a reliable production solution.
A full-stack mindset: You think about the complete experience, from backend services and data to the tools people use. You are willing to work across the stack to solve problems end to end.
A product mindset: You seek to understand the needs of operational teams and customers, use data to guide decisions, and connect technical work to measurable improvements.
Sound technical judgement: You design systems for long-term change and scale, balancing delivery needs with maintainability and operational reliability.
Clear communication and collaboration: You explain technical trade-offs to non-technical partners and work effectively across engineering, product, operations, and data disciplines.
Nice to have: Experience deploying machine learning or LLM-based systems in production, evaluating AI quality and cost, or building operational workflow and case management tools.
No prior AML experience required. Our product leads, operational partners, and data scientists will help you build domain knowledge if you bring curiosity and a willingness to learn.
For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.
We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
If you want to find out more about what it's like to work at Wise visit Wise.Jobs.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Answer a McCoy challenge. Companies hiring in engineering see your response and reach out when they want to talk.
About 15 minutes to prepare and 5 minutes to record. Unlimited retakes and a teleprompter help you answer. No AI training, and you can delete your response anytime.
This isn't an application to Wise.
wise:744000158463587wiseThis posting hasn't been probed by our closure verifier yet. Stream C runs on a rolling schedule against postings approaching the close-decision threshold.
See how we measure for definitions, or our corrections log for known issues. Found something wrong? Flag a correction.