Requirements:

Demonstrated expertise (5+ years) in developing and deploying production-grade AI systems and Machine Learning (ML) in financial risk or fraud domains.
Technical Proficiency: Skilled in Python, capable of delivering production-ready Python services as required; possesses hands-on experience with neural networks and deep learning models; has comprehensive knowledge of machine learning frameworks such as TensorFlow or PyTorch, as well as AI agent frameworks like LLamaIndex and LangGraph; well-versed in LLM orchestration and MCP usage.
Data-driven mindset: skilled in designing data strategies, including data collection, curation, and augmentation, to support model development. Experience with big-data frameworks and working with large scale databases.
Technical leadership and mentorship: demonstrated ability to guide, mentor and level-up teams on technical aspects, fostering a collaborative and innovative work environment.
Excellent communication skills, capable of simplifying complex technical concepts for easy understanding; able to adapt communication style to suit different audiences; can effectively engage and advise both technical and non-technical stakeholders with clarity and logic.
A strong product mindset with the ability to work independently in a cross-functional and cross-team environment.

What you'll be doing:

Innovate and Develop: Lead the development and deployment of machine learning models, including neural networks, anomaly detection, graph-based models, Transformers. Design and build modular detection systems able to detect in an evidenceable way red flags and typologies across different regions where Wise operates.
Lead and Collaborate: Mentor team members and promote adoption of AI workflows for automation across the business. Collaborate with cross-functional teams to integrate data science solutions into AML detection product offerings.
Deploy and Integrate: Develop scalable deployment strategies together with Platform teams and integrate LLMs with AI agents for seamless production use.
Optimize and Evaluate: Conduct large-scale training and hyperparameter tuning, and define performance metrics to ensure high-quality model outputs.
Data Strategy and Management: Design and implement strategies for data collection, curation, and augmentation to support robust model training.
Documentation and Reporting: Communicate complex data findings to non-technical stakeholders effectively. Document the development and maintenance processes for models and features.

Position based in Tallinn, Estonia
Opportunity to work behind the scenes of company transactions and enhance risk detection
Direct impact on Wise's mission and millions of customers
Collaborative work environment with software engineers, data analysts, data scientists, and compliance specialists
Contribute to building a globally scalable AML prevention and detection engine