Senior Software Developer - Senior AI DevOps Engineer

Computacenter; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Computacenter seeks a Senior AI DevOps Engineer who blends software engineering, DevOps, automation architecture, and AI platform expertise to deliver secure, scalable, and maintainable enterprise solutions. You will lead design and implementation of AI and DevOps solutions across cloud-native services.

You will build IaC with Terraform and Ansible, design Azure-based AI platforms, and drive DevOps maturity with Azure DevOps Boards, Repos, and Pipelines while mentoring engineers and establishing

Qualifications

• Senior-level DevOps and cloud delivery experience in enterprise settings.
• Experience designing and implementing AI and automation platforms.
• Proven IaC development with Terraform and Ansible.
• Deep understanding of Azure services and cloud-native architectures.
• Experience mentoring engineers and leading technical discussions.
• Strong stakeholder management and communication skills.

Responsibilities

• Lead design/implementation of enterprise AI and DevOps solutions.
• Define architectures for automation, AI, and cloud-native services.
• Create and maintain IaC using Terraform and Ansible.
• Design Azure-based AI platforms leveraging Azure AI Foundry and Copilot Studio.
• Advance DevOps maturity with Azure DevOps Boards, Repos, and Pipelines.
• Lead architecture reviews and workshops; mentor junior developers.
• Establish development standards, security controls, and deployment best practices.
• Collaborate with ITSM teams to align automation with processes.
• Evaluate emerging technologies including Agentic AI and intelligent automation frameworks.
• Ensure scalable, resilient, and maintainable enterprise solutions.

Skills

DevOps engineering

Azure platform

Terraform

Ansible

Azure DevOps

Agile methodologies

Enterprise integration patterns

Leadership mentoring

Tools

Terraform

Ansible

Azure DevOps

Azure AI Foundry

Copilot Studio

Job description

• 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.