Senior Software Developer - Senior AI DevOps Engineer

• 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.
• Strong 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.