Role: AI DevOpsEngineer
Location: Sheffield, UK (Hybrid)
Hiring type: Contract Role
Role Focus
* End-to-end software delivery lifecycle
* 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
* CI/CD Tools: Jenkins, GitLab CI, Azure DevOps
* Containerization: Docker, Kubernetes
* 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)