Principal MLOps Engineer

Fresher

Job Description

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