AI DevOps Engineer

Overview

Infrastructure as Code (IaC), CI/CD, monitoring, and system reliability

Strong understanding of enterprise environments, controls, and compliance requirements

Ability to manage work and deliverables independently

Ability to support GenAI-enabled engineering practices (e.g., AI-assisted development/operations) while adhering to enterprise governance, security, and risk controls

Key Responsibilities

Design, implement, and maintain CI/CD pipelines (e.g., Jenkins)

Automate infrastructure provisioning and configuration (e.g., Ansible, Terraform)

Ensure high availability, scalability, and security of systems

Collaborate with developers, QA, and IT operations to streamline releases

Troubleshoot deployment, build, and infrastructure issues

Maintain configuration management tools (Ansible)

Ensure compliance with enterprise controls and regulatory requirements

Work independently to deliver on assigned tasks and projects

Utilize Jira and Confluence for work tracking and documentation

Enable and operationalise GenAI/LLM-based tooling in the DevOps toolchain (where approved), including secure access, environment configuration, and integration into CI/CD workflows

Implement guardrails for GenAI usage (e.g., data handling, prompt/content logging where required, secrets protection, model/tool access controls) aligned to enterprise policies

Support reliability and observability for GenAI-enabled services (e.g., monitoring latency, error rates, cost/usage, and drift/quality signals where applicable)

Core Skills

Scripting/Programming: Bash, Python, Shell

Infrastructure as Code: Ansible, Terraform

Source Control: Git, Bitbucket

Security & Compliance: Understanding of DevSecOps, secrets management, enterprise controls, and compliance

Familiarity with Jira and Confluence

GenAI/LLM Fundamentals: Understanding of LLM concepts (prompting, embeddings, RAG, model limitations), and how to apply them safely in engineering workflows

GenAI Tooling & Integration: Experience using and integrating GenAI-assisted tools (e.g., code assistants, chat-based ops assistants) and/or APIs into developer/DevOps workflows

Responsible AI & Data Handling: Awareness of privacy, IP, data classification, and secure usage patterns when working with GenAI tools in enterprise environments

Preferred

Experience supporting GenAI platforms/services in production (e.g., model/API deployment patterns, prompt/version management, evaluation/monitoring, and cost controls)

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