Design, develop, and maintain core product capabilities for Red Hat's multicluster management platform using Go (Golang), Kubernetes, and distributed systems technologies.
Build cloud-native platform services, APIs, controllers, operators, and control-plane components that enable enterprise-scale Kubernetes management.
Design scalable and reusable platform features that address broad customer needs and become part of the product roadmap.
Participate in reviews, technical design discussions, code reviews, testing, debugging, and continuous product improvement initiatives.
Design, optimize, and orchestrate context-building strategies for Large Language Models (LLMs) to improve software engineering workflows.
Build and enhance context pipelines, context harnesses, and evaluation mechanisms that improve the accuracy, determinism, and effectiveness of AI-generated outputs.
Contribute to the evolution of an Agentic Software Development Lifecycle (SDLC) through intelligent agents, autonomous workflows, and AI-native engineering practices.
Apply context engineering techniques to ensure AI tools generate context-aware, reliable, and high-quality software artifacts.
Evaluate and leverage AI-assisted development tools to improve developer productivity, engineering velocity, and software quality.
Help establish best practices for integrating AI into modern software development workflows.
Actively contribute to Open Cluster Management (OCM) and related Kubernetes and CNCF ecosystem projects.

3+ years of software engineering experience building platform products, infrastructure software, cloud-native platforms, or distributed systems.
1+ year of hands-on experience in AI context engineering, context orchestration, prompt engineering, retrieval optimization, or AI-native developer workflows.
Practical experience using AI-assisted development tools such as Claude Code, Gemini Code Assist, GitHub Copilot, Cursor, Aider, Continue, or similar platforms.
Experience with either Go (Golang) development or Kubernetes platform technologies.
Strong understanding of distributed systems, APIs, microservices, and cloud-native architecture principles.
Strong analytical, debugging, troubleshooting, and problem-solving skills.
Familiarity with modern software engineering practices including CI/CD, automated testing, observability, and Agile development methodologies.
Strong communication and collaboration skills with the ability to work effectively in globally distributed teams.
Passion for platform engineering, open source development, and emerging AI technologies.

Software Engineer – AI Context Engineering

Key skills