Why PayNet / Why Now
- National payments infrastructure with real economic and systemic impact
- Organisation entering a phase of greater scale, scrutiny, and performance expectations
- Leadership demanding clearer differentiation, stronger governance, and better data
- People function expected to shape outcomes, not just run processes
TL;DR
- Own production‑grade Machine Learning Operations (MLOps) platforms powering fraud and risk intelligence
- Decide how Machine Learning (ML) models are promoted, rolled back, and governed in production
- Build secure, auditable platforms across Amazon Web Services (AWS) and hybrid environments
- Partner with Data Scientists to turn models into reliable, explainable scoring services
Why This Role Matters
- Fraud models only create value when they are stable, explainable, and production‑ready
- This role governs the boundary between ML innovation and real‑world financial impact
- Engineering decisions here directly affect system resilience and regulatory confidence
- You enable PayNet to scale Artificial Intelligence (AI) without compromising trust
What You Will Actually Do
- Own end‑to‑end MLOps productionisation for fraud and risk intelligence use cases
- Build and operate Continuous Integration / Continuous Deployment (CI/CD) pipelines for model and service release
- Design and enforce model lifecycle management, including versioning, retraining, and redeployment
- Architect and operate secure AWS and on‑premises hybrid infrastructure for ML platforms
- Implement platform standards using Infrastructure as Code (IaC), containerisation, and Identity and Access Management (IAM)
- Ensure deployments meet audit, security, and regulatory requirements without sacrificing stability
Examples of This Role in Practice
- Decide whether a fraud model can be safely promoted during elevated transaction risk
- Design rollback mechanisms when a real‑time scoring service degrades latency
- Convert experimental notebooks into governed, auditable production pipelines
- Balance model accuracy, infrastructure cost, and response time at national scale
What Will Help You Succeed
- Experience building and operating production ML systems, MLOps platforms, or large‑scale DevOps environments
- Strong proficiency in Python for ML pipelines, model packaging, automation, and service integration
- Deep hands‑on expertise with AWS architecture, including secure networking and high‑availability design
- Proven ability to design CI/CD pipelines for ML services with gated releases and controlled promotion
- Experience with IaC tools such as Terraform and container orchestration using Kubernetes and Helm
- Familiarity with distributed workloads (e.g. Apache Spark or Ray) and orchestration tools such as Apache Airflow or Prefect