Devsecops

As a DevSecOps Engineer, should architect the backbone that powers GenAI agents. Should be responsible for building a fortress around LLM orchestration layers and data connectors. Whether deploying to Azure Kubernetes Service (AKS) or Hardened On-Premise Kubernetes clusters, should ensure the infrastructure is immutable, scalable, and compliant. Should own the pipes that allow AI to securely access enterprise data while maintaining strict network isolation and secret management.

Infrastructure as Code (IaC): Architect and maintain multi-environment infrastructure using Terraform, ensuring parity between Cloud (Azure) and On-Prem environments. Kubernetes Orchestration: Manage and tune AKS and on-prem K8s clusters, focusing on high availability, auto-scaling, and resource quotas for GPU/CPU-intensive AI workloads. Hardened Security: Implement a Zero Trust architecture using Azure KeyVault (or HashiCorp Vault for on-prem), Network Security Groups (NSGs), and Private Links to ensure AI traffic never touches the public internet. CI/CD Pipeline Engineering: Design robust, automated pipelines (GitHub Actions, GitLab CI, or Azure DevOps) that integrate security scanning (SAST/DAST) and automated Helm deployments. Identity & Access Management (IAM): Enforce the Principle of Least Privilege (PoLP) using Azure Entra ID (RBAC) and Kubernetes Service Accounts to control agentic access to data. Observability & Monitoring: Setup deep-stack monitoring using Prometheus, Grafana, or Azure Monitor to track the health of AI microservices and agentic loop latencies.