Fraud & Forensics MLOps Engineer

Ovations Talent Sourcing; Johannesburg; Hybrid; ZAR 1,000,000 - 1,800,000; Full time

Job summary

Ovations Talent Sourcing is seeking a Senior Fraud & Forensics MLOps Engineer in Johannesburg. The role is hybrid (3 days in office) and on a 6‑month independent contract. You will drive end‑to‑end fraud model lifecycle from experimentation to secure, scalable production environments.

You will work with Fraud & Forensics, Data Science, Data Engineering, Technology, Risk and InfoSec to deliver monitored, production‑ready solutions and ensure governance, security and compliance across markets.

Qualifications

• Bachelor’s degree in a relevant field such as CS, data analytics, statistics, engineering or finance.
• 5–7+ years in fraud analytics or financial crime analytics with production exposure.
• Experience operationalising analytics models and deploying them in production.

Responsibilities

• Build, deploy and support MLOps solutions for fraud detection and analytics.
• Develop and maintain CI/CD pipelines for model training, testing and deployment.
• Automate model retraining, versioning, deployment and rollback.
• Containerise and deploy models using Docker and Kubernetes.
• Implement model monitoring for performance, drift, latency and health.
• Support model registries, feature stores and experiment tracking.
• Troubleshoot pipeline failures and production defects.
• Provide post‑deployment support and documentation.

Skills

MLOps

CI/CD

Python

Docker

Kubernetes

Git / GitHub

Azure DevOps

Model monitoring

Data analytics

Security governance

Education

Bachelor's degree in Computer Science or related field

Tools

MLflow / Kubeflow

Azure DevOps / Jenkins

SQL

Cloud ML environments

Job description

JOB OPPORTUNITY: SENIOR FRAUD & FORENSICS MLOPS ENGINEER

Location: Johannesburg / Hybrid-3 days in the office

Contract: 6 months independent contract

Industry: Telecommunications / Fintech / Financial Services

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.