Fraud & Forensics MLOps Engineer

• We are looking for an experienced Senior Fraud & Forensics MLOps Engineer to join a leading organisation and support the development, deployment and lifecycle management of fraud detection and financial crime analytics models across multiple markets.
• This is a hands-on technical role focused on taking fraud models from experimentation through to secure, scalable and monitored production environments.

Key Responsibilities

• Build, deploy and support MLOps solutions for fraud detection and financial crime analytics.
• Develop and maintain CI/CD pipelines for model training, testing, deployment and release management.
• Automate model retraining, versioning, deployment, rollback and environment promotion.
• Containerise and deploy models using Docker, Kubernetes or equivalent technologies.
• Implement model monitoring covering performance, data/feature drift, latency, errors and operational health.
• Support model registries, feature stores and experiment tracking.
• Troubleshoot model pipeline failures, deployment issues and production defects.
• Develop reusable deployment templates, automation scripts and monitoring components.
• Support fraud models used for transaction monitoring, anomaly detection, risk scoring and suspicious activity detection.
• Ensure model deployments meet security, governance, audit and compliance requirements.
• Work closely with Fraud & Forensics, Data Science, Data Engineering, Technology, Risk, Compliance and Information Security teams.
• Provide post-deployment support, technical documentation and knowledge transfer.

Requirements

• Bachelor's degree in Computer Science, Data Analytics, Statistics, Engineering, Information Systems, Finance, Risk Management, Forensics or a related field.
• 5–7+ years' experience in fraud analytics, financial crime analytics, fraud detection, transaction monitoring, digital risk, fintech, banking, payments or mobile money.
• Experience with fraud detection models, risk scoring, anomaly detection or transaction monitoring.
• Strong understanding of fraud typologies, risk indicators, false positives, missed detections and model performance.
• Experience working with Data Science, Technology and Data teams to operationalise analytics models.
• Strong understanding of MLOps, CI/CD, model deployment and production support.
• Experience with technologies such as:
• Python
• Git / GitHub
• Azure DevOps / GitHub Actions / Jenkins
• Docker
• Kubernetes
• MLflow / Kubeflow or similar
• Cloud-based ML environments
• SQL and data analytics tools
• Experience in banking, fintech, telecommunications, digital financial services, payments or mobile money will be advantageous.

Advantageous Certifications

• CFE, ACAMS, ICA, FRM, Data Science, Analytics, Model Governance or related certifications.

Ideal Candidate

• We are looking for someone who is analytical, investigative, technically hands-on and fraud-risk aware, with the ability to work across Fraud, Data Science and Technology teams to turn fraud model requirements into working, monitored and production-ready solutions.