MLOps Engineer: ML Risk Platform

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This MLOps role focuses on building and owning the infrastructure for ML models in production at a fintech risk platform. Responsibilities include designing automated ML pipelines, monitoring for data drift and model performance, ensuring compliance with EU AI Act and GDPR, and maintaining Kubernetes-based serving infrastructure. Candidates must have experience in production ML pipelines, Kubernetes, ML lifecycle management, monitoring for ML failures, and working in regulated environments.

• Design and operate automated training, validation, deployment, and rollback workflows
• Build observability for ML-specific failure modes such as data drift, prediction drift, and feature skew
• Maintain full audit trails and model cards for regulatory compliance
• Run Kubernetes-based ML serving on AWS or Azure
• Define SLAs for latency-sensitive scoring models and own incident response
• Optimise cloud spend for GPU training jobs and batch inference workloads

• Production ML pipelines built and operated in a live environment
• Kubernetes in production (EKS, AKS, or GKE)
• ML lifecycle ownership with MLflow, Weights and Biases, or equivalent
• Monitoring for ML-specific failures using Evidently AI, Prometheus, or equivalent