Senior DevSecOps & Platform Engineer

EPAM Systems; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

EPAM Systems Inc. seeks a Senior DevSecOps & Platform Engineer to build and operate secure AI platforms on AKS, leveraging Terraform-based infrastructure and Azure DevOps pipelines. You will implement Landing Zones, standardize networking and security, and automate deployments across teams.

You will collaborate with Cloud Engineers, architects, and developers to enable scalable, secure workloads, maintain build agents, and ensure observability via Azure Monitor and Log Analytics.

Qualifications

• 4+ years of experience in DevSecOps or Platform Engineering.
• Expertise in CI/CD pipelines and automation tools.
• Proficiency in Terraform and Infrastructure as Code.
• Experience deploying and operating Kubernetes / AKS environments.
• Familiarity with Git-based version control systems.
• Background in enterprise cloud environments and platform standardization.
• English proficiency at B2 level or higher.
• Nice to have Experience with CI/CD pipelines for AI/ML workloads.
• Understanding of deploying model inference services on Kubernetes (AKS).
• Experience with containerization for AI workloads.
• Awareness of AI security and secure deployment practices.

Responsibilities

• Implement infrastructure within Azure Landing Zones
• Deploy and configure production-ready AKS clusters
• Design and implement CI/CD pipelines using Azure DevOps
• Automate infrastructure and application deployments using Terraform, Helm, and Kubernetes manifests
• Enable deployment of applications and AI inference services on AKS
• Implement platform standards for networking and security, including VNets, private endpoints, RBAC, and policies
• Integrate CI/CD pipelines with Key Vault for secrets management and with security and policy checks
• Support onboarding of internal teams onto the platform
• Improve platform scalability, reliability, and maintainability
• Configure monitoring and logging using Azure Monitor and Log Analytics
• Manage and maintain build agents, including private agents

Skills

DevSecOps

Platform engineering

CI/CD pipelines

Kubernetes

AKS

Git

English (B2)

CI/CD for AI/ML

AI security

Cloud platforms

Tools

Terraform

Helm

Azure DevOps

Azure

Job description

We are seeking a Senior DevSecOps & Platform Engineer to support the delivery of scalable and secure AI platforms, working closely with Cloud Engineers, architects, and development teams to build and operate shared Azure platform capabilities. You will implement infrastructure and automation aligned with enterprise standards, enabling consistent, secure, and efficient deployment of AI and application workloads on AKS.

Responsibilities

• Implement infrastructure within Azure Landing Zones
• Deploy and configure production-ready AKS clusters
• Design and implement CI/CD pipelines using Azure DevOps
• Automate infrastructure and application deployments using Terraform, Helm, and Kubernetes manifests
• Enable deployment of applications and AI inference services on AKS
• Implement platform standards for networking and security, including VNets, private endpoints, RBAC, and policies
• Integrate CI/CD pipelines with Key Vault for secrets management and with security and policy checks
• Support onboarding of internal teams onto the platform
• Improve platform scalability, reliability, and maintainability
• Configure monitoring and logging using Azure Monitor and Log Analytics
• Manage and maintain build agents, including private agents

Requirements

• 4+ years of experience in DevSecOps or Platform Engineering
• Expertise in CI/CD pipelines and automation tools
• Proficiency in Terraform and Infrastructure as Code
• Experience deploying and operating Kubernetes / AKS environments
• Familiarity with Git-based version control systems
• Background in enterprise cloud environments and platform standardization
• English proficiency at B2 level or higher
• Nice to have Experience with CI/CD pipelines for AI/ML workloads
• Understanding of deploying model inference services on Kubernetes (AKS)
• Experience with containerization for AI workloads
• Awareness of AI security and secure deployment practices