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)

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