About the job
Join d-Matrix, a leader in harnessing the power of generative AI to revolutionize technology. We are dedicated to software and hardware innovation, constantly pushing the limits of what is achievable. Our workplace culture is rooted in respect and collaboration.
We prioritize humility and encourage open communication. Our inclusive team thrives on diverse perspectives, leading to enhanced solutions. If you are passionate about solving complex challenges and are results-oriented, we invite you to explore the exciting opportunities at d-Matrix. Together, we can unlock the vast potential of AI.
Location:
This position is hybrid, requiring in-office attendance at our Bangalore location three days a week.
Role Overview: Senior DevOps Engineer
As a Senior DevOps Engineer, you will play a pivotal role in enhancing the d-Matrix infrastructure, enabling scalability and supporting our comprehensive software and hardware development initiatives. You will collaborate within the d-Matrix DevOps Infrastructure team, focusing on the creation, automation, scaling, and support of core infrastructure that empowers our engineering teams (software, silicon, hardware, QA, research) to operate swiftly and reliably. Your contributions will be crucial in managing and optimizing our CI/CD pipelines, internal services, shared tools, observability stack, and lab environments. This hands-on position will allow you to help establish a global, follow-the-sun DevOps model as the company expands in Bangalore and beyond.
Key Responsibilities:
- Support, monitor, and enhance CI/CD execution environments, runners, and infrastructure utilized by engineering teams.
- Troubleshoot and resolve build issues, pipeline failures, and environmental inconsistencies across various infrastructures (on-prem, cloud, labs).
- Manage and automate configuration of Linux-based systems, containers, and internal services leveraged by development teams using Ansible and Terraform.
- Oversee deployment, upgrade, and reliability workflows for shared tools and internal systems (e.g., GitLab runners, Kubernetes clusters, artifact storage, observability tools).

