2 days ago

Senior Machine Learning Engineer

SurePay

Utrecht

2 days ago

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SurePay, a leading account verification platform, is seeking a Senior Machine Learning Engineer to join a cross-functional team in Utrecht. The role involves building and scaling a sub-millisecond real-time ML processing platform for fraud and AML risk intelligence, requiring 8+ years of experience in software engineering and MLOps.

Core AIHybridFull-timeSeniorAWSAWS SageMaker

Senior Machine Learning Engineer

Apply

Core AIHybridFull-timeSeniorAWS

Salary

Not specified

Work Location

Utrecht, Netherlands, NL

Work Model

Hybrid

Experience Required

8 years

Employment Type

Full-time

Experience Level

Senior: 8+ years

Core Qualifications

Technical (Must-have)

AWSAWS SageMakerTerraformCloudFormationCI/CDPythonMicroservicesReal-time data streamingOnline feature storesPyTorchTensorFlowscikit-learnApache SparkMLOpsInfrastructure as Code

Soft Skills

CommunicationAccountabilityPrototypingAdaptabilityCollaboration

Preferred Qualifications

Technical (Nice-to-have)

Fraud detectionAMLTransactional risk scoringNICE ActimizeRiskShieldFCRMPega

Key Responsibilities

•Partner with Data Scientists to understand the FRI problem space and connect technical implementations with client requirements.
•Support Data Scientists with coding needs, guiding them on packaging and deploying ML models to production.
•Design high-performance microservice APIs with sub-millisecond latency to expose ML models.
•Shape the next iteration of the internal data platform from an MLOps perspective, implementing real-time data streaming and online feature store management.
•Architect, deploy, and maintain end-to-end AWS MLOps infrastructure using Infrastructure as Code and CI/CD pipelines.
•Set up automated monitoring, drift detection, continuous training loops, and model emulation environments.

Machine LearningMLOpsAWSFraud DetectionAMLReal-time ProcessingPythonSeniorHybridUtrecht

Key Responsibilities

•Partner with Data Scientists to understand the FRI problem space and connect technical implementations with client requirements.

•Support Data Scientists with coding needs, guiding them on packaging and deploying ML models to production.

•Design high-performance microservice APIs with sub-millisecond latency to expose ML models.

•Shape the next iteration of the internal data platform from an MLOps perspective, implementing real-time data streaming and online feature store management.

•Architect, deploy, and maintain end-to-end AWS MLOps infrastructure using Infrastructure as Code and CI/CD pipelines.

•Set up automated monitoring, drift detection, continuous training loops, and model emulation environments.