DevOps Engineer - Machine Learning Platform

About the role

We are looking for a DevOps Engineer to build and operate the Azure‑based infrastructure that powers DSK Bank’s AI and Machine Learning platform. You will enable data scientists and engineers to deploy AI solutions securely, reliably and at scale while meeting IT security and audit requirements.

Key responsibilities

• Provision, monitor and maintain Azure infrastructure for ML/AI platforms, ensuring high availability and SLA compliance.
• Implement fully auditable Infrastructure as Code using Terraform, Bicep or ARM templates.
• Improve Azure security posture, apply security best practices and manage patching to satisfy ITSec and audit standards.
• Manage and optimise Azure Kubernetes Service (AKS) clusters for model serving and AI agent deployment.
• Design, implement and maintain CI/CD pipelines for infrastructure and application delivery.
• Monitor performance, troubleshoot incidents, conduct root‑cause analysis and drive preventive actions.
• Collaborate with software engineers, data scientists and ML engineers to optimise reliability, performance and cost.
• Participate in on‑call rotation and follow change‑management processes for production changes.

Required profile

• Minimum 3 years of experience in DevOps, SRE or cloud infrastructure roles.
• Hands‑on experience with cloud platforms (Azure, AWS or GCP).
• Strong knowledge of Linux system administration and scripting.
• Fluent written and spoken English.
• Ability to work in on‑call rotations and handle production incidents.

Required skills

• Microsoft Azure services (VMs, App Services, VNets, NSGs, Load Balancers, Storage, Security).
• Infrastructure as Code tools: Terraform, Bicep, ARM templates.
• Container orchestration: Kubernetes, Azure Kubernetes Service (AKS), OpenShift.
• CI/CD tools: Azure DevOps, GitHub Actions, GitLab CI.
• Scripting languages: Bash, Python.
• Monitoring and logging: Azure Monitor, Prometheus, Grafana, ELK stack.
• Security best practices, compliance and audit processes.