AI DevOps Engineer

Overview

In this role you will own the deployment, operations, and reliability of the AI-SDLC platform as it moves to a centralized hosted service. You will build and run the infrastructure, CI/CD, monitoring, and observability to keep the platform available, secure, and scalable across teams. You’ll automate deployments, maintain pipelines, and support productionization to meet enterprise SLAs and governance. This is a hands-on role with a focus on reliability, security, and cost-conscious scalability in an enterprise context.

Responsibilities

• Own AI-SDLC deployment, operations, and platform reliability (uptime, performance, incident response)
• Manage infrastructure, monitoring, observability, and on-call support
• Build and maintain CI/CD pipelines and automation for platform releases
• Drive the shift to a centralised, hosted AI-SDLC service — scalability, multi-tenancy, cost, and resilience

Key requirements

• CI/CD pipelines experience (GitLab Pipeline / Jenkins & Harness)
• Infrastructure as Code (Terraform)
• Cloud platform experience (AWS ECS/EKS, Lambda, S3, IAM, networking) with container orchestration (Docker + Kubernetes)
• Observability and monitoring (OpenTelemetry, CloudWatch, logging/tracing)
• Scripting/automation (Python and/or Bash)
• Security & compliance operations (secrets management, IAM/least privilege, PCI DSS-aligned controls, auditability)
• Reliability engineering fundamentals (SLIs/SLOs, incident management, cost and capacity management)

• CI/CD pipelines: GitLab, Jenkins, Harness
• Infrastructure as Code: Terraform
• AWS cloud services: ECS/EKS, Lambda, S3, IAM, networking
• Containerization: Docker, Kubernetes
• Observability: OpenTelemetry, CloudWatch
• Scripting: Python, Bash

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