AI & Kubernetes Engineer
Job Description
Role Overview
We are seeking an AI & Kubernetes Engineer to design, build, and optimize our infrastructure by leveraging AI tools, specifically Cursor AI. In this role, you will take ownership of our AI-assisted workflows by understanding, managing, modifying, and creating custom Cursor capabilities and skills to maximize engineering velocity across our Kubernetes (K8s) environments. Additionally, you will leverage AI to drive end-to-end project development and execution independent of the specific technologies, languages, or stacks involved.
Core Responsibilities
• Cursor Skills Management: Audit and understand existing Cursor custom skills and context, ensuring they align with our architecture and engineering standards.
• On-Demand Skill Modification: Make real-time, on-demand modifications and iterative improvements to Cursor skills to resolve immediate development bottlenecks.
• New Skill Creation: Build and deploy brand-new Cursor skills, custom instructions, and .cursorrules from scratch to automate repetitive Kubernetes tasks.
• Tech-Agnostic Project Execution: Utilize AI to rapidly adapt to, develop for, and execute projects across any technology stack or programming language required by the business.
• AI-Driven Project Lifecycles: Leverage AI tools to accelerate the entire project lifecycle, from initial architecture and code generation to testing, cross-functional deployment, and documentation.
• Kubernetes Orchestration: Deploy, scale, and manage Kubernetes clusters while using your customized AI workflows to generate manifests, Helm charts, and K8s configurations.
• AI Tool Optimization: Continually refine how the AI interacts with your codebase, feeding relevant documentation, schemas, and cluster configurations into Cursor for highly accurate outputs.
Required Skills & Qualifications
• Advanced Cursor AI Mastery: Proven experience not just using Cursor, but deeply managing, editing, and creating advanced Cursor behaviors, custom skills, and workspace rules.
• AI Logic & Context Engineering: Strong understanding of defining AI constraints, feeding project-specific context, and fine-tuning AI behavior through configuration.
• Polyglot & Adaptive Mindset: Proven ability to use AI as a force multiplier to quickly master and deliver robust code in unfamiliar technical environments, frameworks, or legacy systems.
• Kubernetes Expertise: Deep knowledge of Kubernetes architecture, kubectl, Helm, and cloud-native infrastructure.
• DevOps Automation: Experience integrating AI-optimized code and scripts into modern CI/CD pipelines.
Source: the employer's careers page. Last checked 2026-10-22. Posted 2026-10-22.
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