Architect and optimize cloud infrastructure and CI/CD systems for AI workloads. Our client is a global technology company specializing in cloud infrastructure, AI operations, and enterprise technology solutions.
About the role
Architect and optimize cloud infrastructure and CI/CD systems for AI workloads. Our client is a global technology company specializing in cloud infrastructure, AI operations, and enterprise technology solutions. Supporting organizations worldwide, the company delivers secure, scalable, and reliable cloud environments, helping businesses optimize infrastructure and accelerate AI adoption.
We are looking for a Senior DevOps Engineer to architect, optimize, and own complex cloud infrastructure and CI/CD systems, including LLMOps pipelines supporting AI/ML workloads. In this role, you will drive technical decisions, design highly available and cost-efficient architectures, and ensure the security, reliability, and performance of infrastructure across multiple services.
You will lead infrastructure design reviews, mentor engineers, and collaborate with cross-functional stakeholders to translate business requirements into technical solutions. You will also lead incident response and root-cause analysis for critical infrastructure issues, implement monitoring for AI workloads, and strengthen security controls for AI endpoints and agentic systems. Participation in an on-call rotation for after-hours and weekend support is expected.
Required Skills
6+ years of experience in technology, with significant ownership of cloud infrastructure and DevOps systems.
4+ years of hands-on experience designing, implementing, and optimizing cloud infrastructure and CI/CD pipelines.
Advanced expertise in cloud platforms, containerization, orchestration, and Infrastructure as Code (IaC).
Strong experience architecting highly available, scalable, secure, and cost-optimized infrastructure.
Experience designing and maintaining CI/CD pipelines for distributed applications and AI/ML workloads.
Understanding of LLMOps principles, including AI model deployment, monitoring, and operational performance. …
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