What you'll be doing:
Support DevOps initiatives by developing and maintaining CI/CD pipelines to automate build and deployment processes, and by improving monitoring and observability across services.
Contribute to maintaining both physical and cloud-based infrastructure, including troubleshooting and resolving hardware and system-level issues.
Debug and analyze complex system-level issues to identify root causes and drive timely resolution.
Contribute to the development of verification infrastructure, tackling complex technical challenges to enhance coverage, scalability, and robustness of our services.
Engage in the full software development lifecycle of verification components, from design and architecture through implementation and validation.
Develop automation to simulate real-world environments and design tests to validate the functionality and performance of the platform.
What we need to see:
Bachelors or Masters degree in Computer Science or Software Engineering.
Proven experience of 5+ years as a software engineer, with a focus on building complex, scalable systems.
Strong programming skills in languages such as Python, Java, C++, or similar.
Solid understanding of software design and architecture principles, with hands-on experience applying them.
Experience developing CI/CD pipelines and contributing to DevOps practices, with a strong passion for automation.
Experience with Linux virtualization (e.g., QEMU/KVM) and container orchestration platforms such as Kubernetes.
Experience working with hardware, bare-metal servers, and infrastructure environments.
Strong communication and collaboration skills, with the ability to work effectively in a team environment.
Ways to stand out from the crowd:
Expert-level proficiency in Python programming.
Strong knowledge of one or more networking domains, such as Ethernet, VLANs, TCP/UDP/IP, QoS, and L2/L3 protocols.
Experience with software release processes, including codebase management, branching strategies, integrations, maintenance, and bug tracking.
Demonstrated ability to quickly learn, adapt, and ramp up on new technologies and domains.
Hands-on experience with advanced AI tools and frameworks, and the ability to apply them to real-world engineering challenges.
