- 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
- Strong 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.
- Strong 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.