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.