Scope of Work

1. Design, develop, and implement realistic cloud infrastructure environments to evaluate AI model proficiency in systems design, deployment, and troubleshooting.Create detailed and reproducible scenarios involving distributed systems, networking, Identity and Access Management (IAM), message queues, persistent storage, observability, rolling deployments, and disaster recovery.Develop deterministic validation tests and golden reference solutions to ensure the reliability and accuracy of reinforcement learning environments.Produce intentionally defective variants and failure scenarios to rigorously test AI model responses and recovery strategies.Document the architecture, edge cases, and operational flows for all developed environments, ensuring clarity and reproducibility for future use.Collaborate with technical leads and project participants to iteratively refine environment specifications and acceptance criteria.Apply DevOps and infrastructure automation practices to deliver scalable, secure, and maintainable solutions for cloud-based systems evaluation.

Preferred Qualifications

1. Proven expertise with backend programming languages, such as C++, Python, Rust, GoLang, JAVA, or JavaScript.Strong practical experience with DevOps, cloud infrastructure, CI/CD pipelines, and automation tools.Demonstrated ability to architect, scale, and secure distributed systems in production-grade environments.Deep understanding of networking, IAM, queues, durable storage, and disaster recovery concepts.