Senior DevOps Engineer | $30-$130/hr Remote
Overview
This remote contractor role offers a chance to apply your DevOps expertise to a unique mission: training next-generation AI systems. You'll design and manage the cloud infrastructure and pipelines that enable AI models to learn and improve, using your existing skills rather than requiring prior AI experience.
What You'll Do6
- 1Build and maintain scalable infrastructure on AWS and GCP using Kubernetes to support AI model training workloads.
- 2Create and manage CI/CD pipelines to streamline integration and delivery across various applications.
- 3Automate deployment, monitoring, and infrastructure management with Python and DevOps best practices.
- 4Work closely with engineering teams to ensure cloud-native architectures are robust, reliable, and performant.
- 5Proactively monitor system health, troubleshoot issues, and implement improvements for performance and security.
- 6Document infrastructure designs and processes clearly for both technical team members and non-technical stakeholders.
Requirements5
- 1Deep expertise with Kubernetes for orchestrating containerized applications at scale.
- 2Hands-on experience with both AWS and GCP cloud platforms.
- 3Strong scripting and automation skills using Python.
- 4Solid understanding of infrastructure-as-code, CI/CD pipelines, and core DevOps principles.
- 5Excellent communication skills and a collaborative mindset, with the ability to share knowledge openly.
Who Should Apply
This role is ideal for an experienced DevOps engineer who thrives on building resilient cloud infrastructure and wants to apply those skills to cutting-edge AI training. You should be comfortable working independently in a remote, cross-functional environment and keen to document and share your work with the team.
Salary Insight
The position is compensated at $30–$130 per hour, depending on experience and qualifications, and is a contractor role.
Required Skills
Application Tip
Emphasize your experience managing large-scale Kubernetes clusters across multi-cloud environments, and consider sharing a brief example of how you optimized a CI/CD pipeline for performance or reliability.
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