About the role: 

Please note, this team is hiring across all levels and candidates are individually assessed and appropriately leveled based upon their skills and experience.

As a DevOps Engineer, you will be critical to designing, provisioning, and managing scalable cloud infrastructure and environments for our Agenti AI platform. You will collaborate closely with application teams to build robust CI/CD pipelines, ensure reliable deployments, and maintain highly available Kubernetes clusters. Your expertise will extend to Infrastructure as Code (IaC), observability, cluster scaling, and release management across multiple environments. You will ensure production environments are secure, scalable, and efficiently managed while continuously improving automation and operational excellence.

What’s in it for you

You will be critical to deploying and managing core infrastructure and platform systems that power our products. This means you won't just maintain existing systems; you will be building and standardizing foundational environments using Infrastructure as Code. Your role is crucial in enabling engineering teams to ship reliably and at scale. If you thrive on solving complex distributed systems challenges, improving deployment velocity, and operating large-scale Kubernetes clusters, this is the environment for you.

What you will be doing

• Work closely with the engineering team, AI/ML engineers to design and architect scalable, secure cloud environments for Agentic Applications using Infrastructure as Code (Terraform).
• Design, implement, and manage CI/CD pipelines to ensure safe, repeatable, and reliable deployments across environments.
• Manage and improve release processes including versioning, rollback strategies, blue/green and canary deployments.
• Provision and manage Kubernetes clusters across multiple environments, ensuring high availability and scalability.
• Implement auto-scaling strategies for infrastructure and workloads to optimize performance and cost.
• Set up and manage monitoring, logging, and alerting systems for infrastructure and application workloads.
• Operate and oversee large Kubernetes clusters supporting production workloads.
• Improve reliability, quality, and time-to-market of our software delivery lifecycle.
• Measure and optimize system performance, proactively identifying bottlenecks and implementing improvements.