Principal Engineer (Platform Engineering)

PayNet (Payments Network Malaysia); Kuala Lumpur; On-site; MYR 120,000 - 170,000; Full time

Job summary

PayNet is seeking an experienced DevOps/Platform Engineer to own and evolve its secure AWS and Kubernetes platforms across Development, UAT, and Production. You will design and operate Kubernetes clusters, deployment patterns, workload scaling, and Helm releases, while building controlled Terraform/Atlantis workflows to preserve traceability.

You will lead GitLab CI/CD design for automated builds, testing, security validation, promotions, and deployments.

Qualifications

• 8+ years in DevOps, Cloud/Platform Engineering or SRE
• Deep production experience with AWS, Kubernetes, Terraform, GitLab CI/CD, Helm
• Strong scripting/automation (Python, shell)
• Experience in regulated environments is a plus

Responsibilities

• Own and evolve secure AWS platforms across Development, UAT, and Production
• Architect and operate Kubernetes clusters, deployment patterns, workload scaling and Helm releases
• Build controlled infrastructure workflows with Terraform and Atlantis
• Lead GitLab CI/CD design for automated build, test, security validation, promotion, deployment, rollback, and release governance
• Drive observability, incident response, root-cause analysis, and cost-effective resource use

Skills

AWS

Kubernetes

Terraform

GitLab CI/CD

Helm

Python automation

Observability

Incident response

Tools

Atlantis

Job description

• Build the secure platform foundations behind PayNet’s fraud intelligence, data, microservices, and machine learning capabilities.
• Shape a critical stage of the PayNet Secure Project as workloads, data volumes, and production demands continue to grow.
• Work where platform decisions must balance speed, resilience, security, regulatory expectations, and long-term sustainability.
• Join a role with the mandate to make sound technical trade-offs, challenge assumptions, and turn architecture into dependable production outcomes.

Why PayNet / Why Now

• Build the secure platform foundations behind PayNet’s fraud intelligence, data, microservices, and machine learning capabilities.
• Shape a critical stage of the PayNet Secure Project as workloads, data volumes, and production demands continue to grow.
• Work where platform decisions must balance speed, resilience, security, regulatory expectations, and long-term sustainability.
• Join a role with the mandate to make sound technical trade-offs, challenge assumptions, and turn architecture into dependable production outcomes.

TL;DR

• Own the secure AWS and Kubernetes platform that supports fraud, data, microservices, and machine learning workloads.
• Lead hands-on decisions across Terraform, Atlantis, GitLab CI/CD, Helm, Python automation, observability, and release controls.
• Drive reliability, scalability, security, auditability, and cost-conscious use of platform resources across Development, UAT, and Production.
• Enable production-grade MLOps while reducing dependency on senior architects through clear judgment and accountable execution.

Why This Role Matters

• Platform reliability directly affects the stability and responsiveness of systems supporting fraud intelligence and live model operations.
• Secure, traceable infrastructure and deployment decisions are essential in a regulated and security-sensitive environment.
• Growing data workloads require deliberate architecture that scales without adding unnecessary operational complexity.
• Strong technical ownership will create faster decisions, clearer accountability, and more sustainable delivery across engineering and data teams.

What You Will Actually Do

• Own and evolve secure, highly available AWS platforms across segregated Development, UAT, and Production environments.
• Architect and operate Kubernetes clusters, deployment patterns, workload scaling, resource optimisation, containers, and Helm releases.
• Build controlled infrastructure workflows with Terraform and Atlantis, preserving traceability of infrastructure and configuration changes.
• Lead GitLab CI/CD design for automated build, test, security validation, promotion, deployment, rollback, and release governance.
• Drive observability, incident response, root-cause analysis, performance improvement, resilience, and cost-effective resource use.
• Partner with Data Science, ML, security, networking, and infrastructure teams to enable secure model deployment and lifecycle operations.

Examples of This Role in Practice

• A deployment introduces instability in Production: lead diagnosis, decide the rollback path, and drive a sustainable fix rather than a temporary workaround.
• A data workload grows beyond 100GB per week: evaluate scaling options, make the trade‑off explicit, and evolve the platform without over‑engineering it.
• A new tool could accelerate delivery but open‑source adoption is restricted: assess the security and governance implications and recommend a viable path.
• A live model needs lower latency and stronger monitoring: align platform, ML, and observability decisions to improve production performance and control.
• An existing architecture decision is unclear: review the standards and decision records, challenge assumptions constructively, and document the chosen direction.

What Will Help You Succeed

• At least 8 years of relevant experience in DevOps, Cloud Engineering, Platform Engineering, Site Reliability Engineering, or related infrastructure roles.
• Deep production experience with AWS, Kubernetes, Terraform, GitLab CI/CD, Helm, Python automation, and mission-critical systems.
• Strong judgment in cloud architecture, networking, IAM, secrets management, environment segregation, deployment controls, and security trade-offs.
• Practical strength in monitoring, alerting, troubleshooting, performance optimisation, incident response, and root-cause analysis.
• Ability to explain why systems are designed as they are, question assumptions, decide with incomplete information, and communicate clearly with stakeholders.
• Useful exposure includes regulated environments, large‑scale or distributed data processing, and production MLOps tools or lifecycles.