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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Platform Engineer - **Company:** Wise's Fincrime - **Location:** UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Airflow, Monitoring of Systems, Intrusion Detection and Prevention, Python (Programming Language), Machine Learning, Azure Machine Learning, SQL Databases, Data Ingestion, Deep Learning, Model Validation, Kotlin, Kubernetes, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/senior-ml-platform-engineer-ii-financial-crime-transferwise-9017642 ## About the Role * Experience building ML platform infrastructure in production - training pipelines, model serving, evaluation frameworks, or monitoring systems. Infrastructure that other teams depend on, not individual model work. * Strong software engineering fundamentals - you build reliable, well-tested, maintainable systems. Python, Kotlin/Java, SQL. * Experience with ML orchestration (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment * End-to-end understanding of the ML lifecycle - data ingestion through training, packaging, serving, and monitoring - and knowledge of where things break * A product mindset for internal tooling - you think about data scientists as users and build for adoption, not just functionality Nice to Have: * Model serving at scale - latency optimisation, ONNX packaging, canary deployments for models * Experience in FinCrime, fraud, AML, or regulated environments where audit trails and model governance are non-negotiable * Experience with model monitoring and drift detection systems in production * Track record of migrating teams from manual ML workflows to platform-based approaches ## Description * Wise is one of the fastest-growing global financial platforms, and as we scale, so does the sophistication of the ML systems protecting every transaction. Our Risk ML team is building the model lifecycle platform that makes it possible to develop, deploy, and monitor ML models for financial crime detection - reliably, reproducibly, and at scale. * We're looking for a Senior ML Platform Engineer to build this platform from the ground up. You'll design the infrastructure that turns model development from a bespoke, manual process into a scalable, standardised one - so our data and applied scientists can focus on improving detection rather than managing operations. This is a greenfield build with strong investment and direct engagement from Wise's senior leadership. How we work: * Risk ML sits within Wise's FinCrime organisation, owning the full ML and AI foundation for financial crime detection. We're scaling into three dedicated pillars - Feature Platform, Learning Loop, and Risk Modelling. You'll sit in Risk Modelling, building the platform layer that makes scaling our detection capabilities possible. * You'll work closely with data scientists, feature platform engineers (upstream infrastructure), and Wise's central ML platform team (shared foundations). We value engineers who build for adoption - internal platforms succeed when teams want to use them. What will you be working on? * Designing and building the declarative training pipeline - standardised, config-driven model training that any data scientist can use without writing deployment code * Building model packaging and serving abstraction - a unified interface that handles multiple model types (classical ML, deep learning, emerging architectures) through a consistent API * Implementing the model evaluation framework - standardised metrics, reproducible comparison, and automated validation gates * Building model monitoring - drift detection, performance degradation alerts, automated retraining triggers, and full audit trails for regulatory compliance * Owning the integration layer with Wise's central ML infrastructure - aligning on boundaries so FinCrime-specific lifecycle tooling builds cleanly on shared foundations * Maximising data science productivity - your platform's success is measured by how much time shifts from operational maintenance to improving detection performance ## Related Videos - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Kotlin Multiplatform - True power of native code reuse](https://www.wearedevelopers.com/videos/4-kotlin-multiplatform-true-power-of-native-code-reuse) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [ I Gave a Video Editor More Autonomy Than a Trading Bot. 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