Senior Infrastructure Engineer
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Job Description
Senior Engineer shaping AI infrastructure solutions for Kubernetes-focused services at Mirantis. Mentoring developers and engaging with open source communities while leading AI project development.
Responsibilities:
- Mentor junior developers, sharing your expertise and experience to help them grow professionally
- Leverage your expertise in AI, AI Agents, distributed systems, APIs, and AI-native architectures to make the best decisions on code and systems architectures
- Actively engage with open source communities to cultivate strong relationships, address technical challenges, and champion the adoption of our software solutions
- Partner with internal development teams to ensure the seamless integration of open source contributions into our products and services
- Maintain exemplary standards for all open source contributions, ensuring adherence to best practices in testing, documentation, and long-term maintainability
Requirements:
- 5+ years in software development with significant experience in Python, Golang, and/or Rust
- Excellent problem-solving skills and ability to troubleshoot complex issues
- Experience and understanding of AI-powered co-development tools such as Cursor IDE, Claude Code, Cline, Aider.Chat or similar
- Strong understanding of open source best practices, including code quality, testing, and documentation
- Deep experience with all forms of APIs
- Utilize Docker and Kubernetes for deploying, managing, and scaling microservices
- In-depth knowledge of Kubernetes, including: Overall Kubernetes, Cluster API, CNI (Container Network Interface), CSI (Container Storage Interface), Telemetry
- Strong interpersonal, communication and collaboration skills
- Exceptional problem-solving capabilities with proven ability to diagnose and resolve complex technical issues
- Participation in open source projects as a contributor or maintainer is highly desirable.
- Extensive experience developing Kubernetes operators and leveraging Kubernetes client libraries
- Comprehensive understanding of AI infrastructure components: inference systems, Agentic workflows, autonomous AI agents, Model Context Protocol (MCP), serverless AI functions, and related technologies
- Polyglot with little or no bias for specific programming languages.
Benefits:
- Professional development and training
- Attend conferences and working groups
- Customized workstation (macOS, Windows)
- Competitive compensation package with strong benefits plan and stock options
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