The Senior AI DevOps Engineer role combines software engineering, DevOps, automation architecture, and AI platform expertise to deliver secure, scalable, and maintainable solutions.
What you'll do
Lead the design and implementation of enterprise AI and DevOps solutions.
Define solution architectures supporting automation, AI, and cloud-native services.
Create and maintain Infrastructure as Code (IaC) solutions using Terraform and Ansible.
Design and implement Azure-based AI and automation platforms leveraging Azure AI Foundry and Copilot Studio.
Drive DevOps maturity through Azure DevOps Boards, Repos, and Pipelines.
Lead technical discussions, reviews, and architecture workshops.
Mentor junior developers and provide technical guidance to project teams.
Establish development standards, security controls, and deployment best practices.
Collaborate with service management teams to align automation solutions with ITSM processes.
Evaluate emerging technologies including Agentic AI and intelligent automation frameworks.
Ensure delivery of scalable, resilient, and supportable enterprise solutions.
What you'll need
Essential
Robust experience in DevOps engineering and cloud solution delivery.
Deep understanding of Azure platform services.
Experience with Solution Architecture and enterprise integration patterns.
Expertise in Terraform and Ansible.
Experience with Azure AI Foundry and Copilot Studio.
Strong knowledge of Agile and DevOps methodologies.
Experience delivering enterprise-scale automation solutions.
Robust stakeholder management and communication skills.
Experience leading technical teams and mentoring engineers.
Desirable
Knowledge of Agentic Automation frameworks.
Experience integrating automation with ServiceNow and ITSM processes.
Solid working knowledge of Azure DevOps Boards, Repos, and Pipelines.
Experience with AI governance and responsible AI principles.
Exposure to enterprise platform engineering practices.
Critical Success Factors
Successful delivery of enterprise-scale AI and automation platforms.
Establishment of technical standards and engineering best practices.
Effective mentoring and development of engineering capability.
High-quality architecture and solution design outcomes.
Continuous innovation through adoption of emerging AI technologies.