SRE - Remote

Overview

Dream is a pioneering AI cybersecurity company delivering revolutionary defense through artificial intelligence. Our proprietary AI platform creates a unified security system safeguarding assets against existing and emerging generative cyber threats. Dream's advanced AI automates discovery, calculates risks, performs real-time threat detection, and plans an automated response. With a core focus on the "unknowns," our AI transforms data into clear threat narratives and actionable defense strategies.

In Short

• Lead initiatives to enhance product reliability and system readiness.
• Design and implement sophisticated monitoring solutions to ensure high availability and performance of our production platform.
• Oversee and refine the entire product reliability pipeline.
• Proactively troubleshoot and resolve issues across development, production, and testing environments.
• Champion an "Everything as Code" approach using a wide range of technologies including Ansible, Terraform, and Kubernetes.
• Develop and maintain advanced tools for automation, deployment, monitoring, and operations.
• Exhibit excellent communication and interpersonal skills to effectively collaborate within the team and across departments.
• Promote best practices in reliability and system operations.

Requirements

• Extensive experience configuring and automating monitoring tools.
• At least 4-5 years of experience as a DevOps or Site Reliability Engineer.
• Strong leadership experience, preferably as a Team Lead or Senior Engineer.
• In-depth knowledge of microservices architectures and technologies such as Kubernetes.
• Comprehensive understanding of cloud & on-prem environments and hybrid solutions.
• Proficiency with major cloud providers: AWS, GCP, Azure.
• Advanced experience with CI/CD technologies including Jenkins, GitHub Actions, and ArgoCD.
• Proficient coding and scripting capabilities in Python, Bash, or similar languages.
• Strong team player with proven ability to lead and inspire.

Benefits

• Background in working with AI components (training, inference, serving).