Job Description
Job Title: Site Reliability Engineer
Job Type: Contractor
Location: Remote
Job Summary:
Join our customer's team as a Site Reliability Engineer for a specialized, high-intensity project centered on training and optimizing AI models within cutting-edge containerized infrastructures. This terminal-intensive engagement demands a systems-first approach, real-time troubleshooting, and dynamic process recovery, offering significant potential for future extension or transition into advanced phases for standout performers.
Key Responsibilities:
• Lead the deployment, monitoring, and recovery of complex, containerized AI training environments using advanced terminal techniques.
• Proactively identify, diagnose, and resolve infrastructure bottlenecks and failures in long-running processes.
• Orchestrate resilient system builds and infrastructure management, ensuring stability and optimal resource utilization.
• Collaborate closely with engineering teams to refine CI/CD pipelines and automate routine operational tasks.
• Manage and optimize filesystem structures, networked storage, and process scheduling in Dockerized sandboxes.
• Conduct rapid mid-execution replanning during error states and unforeseen runtime issues.
• Document best practices, emergent solutions, and contribute to knowledge transfer across the team.
Required Skills and Qualifications:
• Demonstrated expert proficiency with terminal-based problem solving and complex system administration.
• Mastery of dynamic infrastructure recovery and long-running operational process management.
• Deep expertise in containerized environments (e.g., Docker, Kubernetes) and sandbox orchestration.
• Strong Python skills, with the ability to script, automate, and debug real-world production systems.
• Proficiency in Bash and familiarity with JavaScript/TypeScript, Go, Rust, C/C++.
• Experience with build systems, package managers, databases, version control, and cryptography tools.
• Adept at troubleshooting, documenting, and replanning in high-velocity technical environments.
Preferred Qualifications:
• Background in machine learning operations or AI infrastructure.
• Familiarity with ML frameworks and distributed computing.
• Experience supporting multi-phase, high-intensity engineering projects.