AI-Powered DevOps Engineer to $5500

We're hiring a DevOps Engineer who thinks in templates, not tickets. You'll design fully automated, repeatable infrastructure — from bare metal to production - using IaC, VM templating, and AI-assisted workflows. If you'd rather teach a machine to do the work than do it twice, this is your role.

Client requirements:

Overview

We are seeking a DevOps Engineer with strong platform engineering capabilities and a forward-looking approach to AI-driven development practices. The role focuses on delivering rapid, repeatable, and fully automated infrastructure and deployment environments.

AI is rapidly transforming DevOps. Candidates must be open to, and ideally experienced in, leveraging AI tools (e.g., Codex) to automate tasks such as security remediation, dependency management, and code generation. We are specifically looking for engineers who embrace AI-assisted workflows rather than relying solely on traditional manual scripting approaches.

Core Responsibilities

• Design and implement rapid, repeatable deployment environments
• Build and manage full lifecycle infrastructure automation, including provisioning, upgrading, testing, and teardown
• Enable deterministic, time-bound deployments through infrastructure abstraction
• Develop and maintain Infrastructure as Code (IaC) for scalable environments
• Support parallel environment provisioning for testing and delivery workflows
• Ensure secure handling of credentials and secrets across environments
• Contribute to platform engineering from bare infrastructure upwards, not limited to managed services

Deployment & Platform Approach

The role requires practical implementation of infrastructure abstraction to enable efficient deployment workflows.

Candidates should understand and implement approaches such as:

• Transitioning from manual workflows:
  • OS cleanup → configure → install → test → teardown → repeat
• To automated, template-driven workflows:
  • Template → clone VMs → bootstrap cluster → test → destroy

This approach enables:

• Provisioning in minutes rather than hours or days
• Deterministic builds using golden images
• Parallel environment execution
• Near-instant teardown
• Clean integration with Infrastructure as Code

A strong understanding of trade-offs between bare-metal and virtualised deployments is required, including performance, reproducibility, and lifecycle management considerations.

Required Technical Skills

Infrastructure & Virtualisation

• Hypervisor platforms: Proxmox, Nutanix AHV, VMware
• VM templating, cloning, and lifecycle automation
• Design of infrastructure abstraction layers for repeatable deployments

Infrastructure as Code & Automation

• Terraform or OpenTofu
• Cluster API (or equivalent) for full cluster lifecycle management
• Packer for image-based infrastructure

Configuration & Version Control

• Ansible for configuration management
• Git for version control and workflow management

Networking

• Infrastructure networking including:
  • VLANs
  • L2/L3 segmentation
  • DNS automation

Security & Secrets Management

• Vault (or equivalent)
• Secure credential and secret handling across environments

Systems Engineering

• Strong Linux systems engineering capability, including:
  • Drivers
  • cgroups
  • GPU passthrough
  • Performance tuning

AI & DevOps Capability

• Willingness and readiness to adopt AI-assisted DevOps practices
• Preferably hands-on experience using AI tools (e.g., Codex) for:
  • Automated code generation
  • Security issue remediation
  • Dependency management
• Open mindset toward evolving DevOps practices driven by AI innovation

Experience Requirements

• Experience building platforms from bare infrastructure upwards
• Proven ability to design and implement repeatable, automated deployment environments
• Strong understanding of real-world deployment constraints and optimisation strategies

Key Competencies

• Automation-first mindset
• Strong problem-solving and systems thinking
• Focus on reproducibility and reliability
• Ability to balance performance, scalability, and operational efficiency
• Adaptability to rapidly evolving tooling, particularly in AI-driven DevOps