Senior Technical Architect for AI Model Training

Apply senior platform engineering and production operations expertise to design realistic cloud infrastructure challenges that train and evaluate next-generation AI systems. You will create Reinforcement Learning environments that test an AI model''s ability to design, deploy, secure, scale, troubleshoot, and recover production-grade cloud systems. No prior AI experience is required, the role prioritizes hands-on production ownership and domain expertise.

• Create realistic cloud infrastructure tasks that cover distributed systems, networking, security, scalability, and reliability.
• Design and build reproducible, containerized environments, including valid golden reference solutions and intentionally defective variants for evaluation.
• Define measurable requirements across infrastructure configuration, deployed topology, and runtime behavior.
• Develop deterministic integration, load, security, failure-injection, deployment, and recovery tests to validate model behavior.
• Build Reinforcement Learning environments that exercise IAM, queues, durable storage, observability, rolling deployments, and disaster recovery scenarios.
• Debug environments, document technical decisions, and review and improve tasks created by other experts.

• Senior-level experience in technical architecture, cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE, including personal ownership of a production platform.
• Strong knowledge of distributed systems, scalable APIs, queues, autoscaling, durable storage, and partial-failure scenarios.
• Practical experience with IAM, private networking, least-privilege access, and service-to-service security.
• Experience with observability, measurable SLOs, rolling deployments, rollback strategies, and disaster recovery.
• Ability to write infrastructure automation or testing tools and to debug containerized environments using a relevant programming language.

• Experience with Terraform or OpenTofu.
• Experience with AWS, Azure, GCP, Kubernetes, or multi-cloud infrastructure.
• Experience building internal developer platforms, edge infrastructure, or shared platform services.
• Familiarity with chaos engineering, fault injection, local cloud emulators, or resilience testing.
• Experience creating technical evaluations, automated grading systems, or AI training/evaluation environments is helpful but not required.

• Role type, remote contractor.
• Output-based compensation, experts are paid per task that meets project specifications.
• Time required to complete work varies by expert experience and workflow, minimum submission requirements apply.
• Experts must submit a minimum number of tasks per week.
• Assignments and volume depend on project availability and may vary over time.

• Pay range: 60 to 130 hourly.
• Compensation is paid per completed task that meets the project specifications, rather than a fixed hourly guarantee.

• Apply and complete screening questions.
• Complete an AI interview, approximately 30 minutes, which will be reviewed by recruiters.
• Final review by the hiring manager after recruiter evaluation.

The source specifies engagement as a contractor and remote work. No additional work-authorization or visa sponsorship information was provided.