Summary
✨ AI‑Generated
Join a senior engineering team building an enterprise-grade machine learning platform for real-time risk intelligence. You will help transform experimental data science into resilient production systems capable of split-second decisions and extremely low latency. The role involves close collaboration across engineering, data science, and product disciplines.
Highlights
Work on a strategic machine-learning platform supporting real-time fraud and risk intelligence. The role offers significant technical ownership, collaboration with engineers, data scientists and product managers, and the opportunity to build highly resilient, ultra-low-latency production systems.
Description
About SurePay
Founded in 2016, SurePay is the leading account verification platform, relied upon by 250+ banks and thousands of organizations, to prevent fraud, misdirected payments, and reputational damage.
Headquartered in Utrecht, we are scaling our platform and our team across Europe and beyond.
About the role
As part of SurePay’s strategic growth, we are expanding beyond our core Account Verification services to launch an integrated suite of actionable Fraud and AML risk intelligence products.
To drive this, we have established a dedicated domain: Fraud Risk Intelligence.
You will join a focused cross-functional team of Engineers, Data Scientists, and Product Managers developing an enterprise-grade Machine Learning Platform.
Fraud detection requires split-second evaluation.
The business goal of this role is to build, scale, and maintain a resilient, sub-millisecond real-time ML processing platform.
You will solve the critical challenge of converting experimental data science logic into high-performance, automated production systems—combining supervised risk models, unsupervised anomaly detection, and rule-based decision engines to stop financial crime in real time.
Responsibilities include:
Product Discovery & Sparring: Partner with Data Scientists to understand the FRI problem space, discover new product possibilities, and connect technical implementations directly with client requirements and business context.Data Science Enablement: Support Data Scientists with their coding needs, guiding them on how to package and deploy ML models to production in a clean, safe, modular, and reproducible way (moving beyond raw notebook handoffs).API & Product Integration: Expose ML models to software engineers by designing high-performance microservice APIs with sub-millisecond latency, evolving new FrAML products according to planned market releases.Data Platform Evolution: Actively shape the next iteration of our internal data platform from an MLOps perspective, implementing real-time data streaming, aggregation points, and online feature store management.MLOps & Infrastructure Ownership: Architect, deploy, and maintain end-to-end AWS MLOps infrastructure using Infrastructure as Code (IaC), CI/CD pipelines, model emulation, and AWS SageMaker.Model Emulation & Operations: Set up automated monitoring, drift detection, continuous training loops, and model emulation environments to ensure low-latency API performance under heavy transactional loads.
About you
We are seeking a Senior ML Designer/Engineer with 8+ years of relevant software engineering and MLOps experience, with a proven track record of bringing ML models to production at scale.
Someone who brings a practical balance of software engineering, cloud infrastructure, and data science literacy.
You thrive in a discovery-driven environment, managing ambiguity to build rapid, non-over-engineered prototypes that scale cleanly.
If you have an industry background in Fraud, AML, or transactional risk scoring systems and/or familiarity with risk platforms such as NICE Actimize, RiskShield, FCRM, or Pega, that would be highly welcomed.
Furthermore,
AWS & MLOps Infrastructure: Deep hands-on experience with AWS SageMaker, Infrastructure as Code (Terraform/CloudFormation), and CI/CD pipelines for ML models.Production Software Engineering: Expert-level Python skills with experience building scalable microservices and sub-millisecond latency APIs.Real-Time Data Architectures: Hands-on experience designing real-time/streaming data pipelines and online feature stores (moving beyond batch-only processing).ML Frameworks: Practical proficiency packaging and deploying models using PyTorch, TensorFlow, Scikit-learn, or Apache Spark.Ability to quickly prototype, iterate, and adapt solutions within evolving, RFP-driven product requirements.Strong sense of accountability for end-to-end system resilience, from architectural design to operational maintenance.Excellent communication skills to interface effectively between Data Science, Data Engineering, and Product Leadership.
What we offer
Competitive salary + 8% personal benefits budget (use it on training, more time off, or your salary)25 holidays Hybrid working setupMacBook Pro, iPhone, and all the tech you needTravel costs covered (NS Business Card)Pension planA culture of ownership, innovation, and autonomyFriday drinks, offsites, and a quarterly company meetupA diverse team of 35+ nationalities, driven by impact
