DevOps Lead

As a DevOps Lead at Weekday's client, your primary responsibilities will encompass designing and optimizing cloud infrastructure on Google Cloud Platform (GCP), managing CI/CD pipelines for efficient deployments, and overseeing Kubernetes clusters for high availability. You will also implement MLOps practices for AI/ML model handling, enhance the security and performance of production systems, and collaborate with engineering teams to refine DevOps workflows.

To qualify for the DevOps Lead role at Weekday's client, candidates should possess at least 4 years of hands-on experience in DevOps or Cloud Infrastructure, specifically with strong expertise in GCP and Kubernetes. In-depth knowledge of CI/CD pipeline development, MLOps, and scripting skills in languages like Python or Bash are essential. Additionally, familiarity with monitoring tools like Prometheus or Grafana is beneficial.

DevOps Leads at Weekday's client primarily work with cutting-edge technologies on Google Cloud Platform (GCP). This involves leveraging tools for automation, such as Terraform and Helm, and managing Kubernetes clusters for deployment. Knowledge of CI/CD tools like GitHub Actions and ArgoCD is also expected to ensure optimal performance and reliability.

A DevOps Lead plays a crucial role in contributing to AI applications at Weekday's client by implementing robust infrastructure and practices. Through the deployment of scalable AI models via MLOps, enhancing system performance, and ensuring security measures are in place, a DevOps Lead significantly boosts the efficiency and reliability of AI-driven solutions in production.

Success as a DevOps Lead at Weekday's client requires a blend of technical and soft skills. Key technical skills include expertise in cloud infrastructure, especially GCP, Kubernetes, CI/CD practices, and scripting. Soft skills such as strong problem-solving abilities, communication, and collaboration are essential, especially in a fast-paced startup setting where teamwork drives success.

When preparing for this question, focus on your specific experiences with GCP. Mention the projects you've worked on, the services you utilized (like Compute Engine, Cloud Storage, etc.), and how you ensured security and scalability within those projects.

In your response, be sure to outline the tools you've used, such as Jenkins or GitHub Actions, and describe the steps you took to configure the pipeline. Highlight any challenges you faced and how you overcame them to deliver smooth and efficient deployments.

Discuss your experience with tools like Prometheus and Grafana. Mention specific strategies you've implemented for scaling applications and maintaining uptime, as well as how you approach troubleshooting issues within your clusters.

Share a specific instance where you encountered a complex issue. Detail the steps you took to diagnose the problem and the resolution process, emphasizing how your solution improved system performance or reliability.

Highlight key principles you adhere to within MLOps, such as versioning models, monitoring performance, and automating training pipelines. Share any tools you use and how these practices have impacted your previous projects.

Discuss your experience with tools like Terraform or Ansible. Explain how you utilize these tools to create repeatable and consistent environments, and share any specific projects where automation improved efficiency.

Emphasize your understanding of cloud security fundamentals. Discuss practices like identity and access management, network security groups, and compliance checks that you've implemented in past roles to safeguard cloud resources.

Talk about your experience in fostering communication between DevOps and engineering teams. Mention specific tools like Slack, JIRA, or Confluence that you utilized to streamline collaboration, along with how you address feedback and drive improvements.

Discuss the types of scripts you've written (e.g., in Python or Bash) and how they facilitate automating processes. Share examples of tasks you've automated and how this has helped optimize your workflow.

Discuss the logging and monitoring tools you have experience with, such as ELK stack or Datadog. Give examples of how you have implemented logging solutions to enhance observability and how this has aided in identifying and resolving issues.